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Social Network Analysis: History, Concepts, and Research

• First Online: 15 October 2010

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• Mingxin Zhang 2  

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Social network analysis (SNA), in essence, is not a formal theory in social science, but rather an approach for investigating social structures, which is why SNA is often referred to as structural analysis [1]. The most important difference between social network analysis and the traditional or classic social research approach is that the contexts of the social actor, or the relationships between actors are the first considerations of the former, while the latter focuses on individual properties. A social network is a group of collaborating, and/or competing individuals or entities that are related to each other. It may be presented as a graph, or a multi-graph; each participant in the collaboration or competition is called an actor and depicted as a node in the graph theory. Valued relations between actors are depicted as links, or ties, either directed or undirected, between the corresponding nodes. Actors can be persons, organizations, or groups – any set of related entities. As such, SNA may be used on different levels, ranging from individuals, web pages, families, small groups, to large organizations, parties, and even to nations.

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Guanxi, used to describe a personal connection between two people in which one is able to prevail upon another to perform a favor or service, or be prevailed upon, is a central concept in Chinese society.

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Zhang, M. (2010). Social Network Analysis: History, Concepts, and Research. In: Furht, B. (eds) Handbook of Social Network Technologies and Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7142-5_1

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Annual Review of Organizational Psychology and Organizational Behavior

Volume 9, 2022, review article, new developments in social network analysis.

• Daniel J. Brass 1
• View Affiliations Hide Affiliations Affiliations: LINKS Center for Social Network Analysis, Department of Management, Gatton College of Business and Economics, University of Kentucky, Lexington, Kentucky, USA; email: [email protected]
• Vol. 9:225-246 (Volume publication date January 2022) https://doi.org/10.1146/annurev-orgpsych-012420-090628
• First published as a Review in Advance on October 26, 2021
• Copyright © 2022 by Annual Reviews. All rights reserved

This review of social network analysis focuses on identifying recent trends in interpersonal social networks research in organizations, and generating new research directions, with an emphasis on conceptual foundations. It is organized around two broad social network topics: structural holes and brokerage and the nature of ties. New research directions include adding affect, behavior, and cognition to the traditional structural analysis of social networks, adopting an alter-centric perspective including a relational approach to ego and alters, moving beyond the triad in structural hole and brokerage research to consider alters as brokers, expanding the nature of ties to include negative, multiplex/dissonant, and dormant ties, and exploring the value of redundant ties. The challenge is to answer the question “What's next in social network analysis?”

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The role of social network analysis in social media research.

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Nie, Z.; Waheed, M.; Kasimon, D.; Wan Abas, W.A.B. The Role of Social Network Analysis in Social Media Research. Appl. Sci. 2023 , 13 , 9486. https://doi.org/10.3390/app13179486

Nie Z, Waheed M, Kasimon D, Wan Abas WAB. The Role of Social Network Analysis in Social Media Research. Applied Sciences . 2023; 13(17):9486. https://doi.org/10.3390/app13179486

Nie, Zhou, Moniza Waheed, Diyana Kasimon, and Wan Anita Binti Wan Abas. 2023. "The Role of Social Network Analysis in Social Media Research" Applied Sciences 13, no. 17: 9486. https://doi.org/10.3390/app13179486

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Title: social network analysis: from graph theory to applications with python.

Abstract: Social network analysis is the process of investigating social structures through the use of networks and graph theory. It combines a variety of techniques for analyzing the structure of social networks as well as theories that aim at explaining the underlying dynamics and patterns observed in these structures. It is an inherently interdisciplinary field which originally emerged from the fields of social psychology, statistics and graph theory. This talk will covers the theory of social network analysis, with a short introduction to graph theory and information spread. Then we will deep dive into Python code with NetworkX to get a better understanding of the network components, followed-up by constructing and implying social networks from real Pandas and textual datasets. Finally we will go over code examples of practical use-cases such as visualization with matplotlib, social-centrality analysis and influence maximization for information spread.

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Social Network Analysis: A brief theoretical review and further perspectives in the study of Information Technology

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The role of social network analysis as a learning analytics tool in online problem based learning

• Mohammed Saqr   ORCID: orcid.org/0000-0001-5881-3109 1 , 2 &
• Ahmad Alamro 3  

BMC Medical Education volume  19 , Article number:  160 ( 2019 ) Cite this article

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Social network analysis (SNA) might have an unexplored value in the study of interactions in technology-enhanced learning at large and in online (Problem Based Learning) PBL in particular. Using SNA to study students’ positions in information exchange networks, communicational activities, and interactions, we can broaden our understanding of the process of PBL, evaluate the significance of each participant role and learn how interactions can affect academic performance.

The aim of this study was to study how SNA visual and mathematical analysis can be sued to investigate online PBL, furthermore, to see if students’ position and interaction parameters are associated with better performance.

This study involved 135 students and 15 teachers in 15 PBL groups in the course of “growth and development” at Qassim University. The course uses blended PBL as the teaching method. All interaction data were extracted from the learning management system, analyzed with SNA visual and mathematical techniques on the individual student and group level, centrality measures were calculated, and participants’ roles were mapped. Correlation among variables was performed using the non-parametric Spearman rank correlation test.

The course had 2620 online interactions, mostly from students to students (89%), students to teacher interactions were 4.9%, and teacher to student interactions were 6.15%. Results have shown that SNA visual analysis can precisely map each PBL group and the level of activity within the group as well as outline the interactions among group participants, identify the isolated and the active students (leaders and facilitators) and evaluate the role of the tutor. Statistical analysis has shown that students’ level of activity (outdegree r s (133) = 0.27, p  = 0.01), interaction with tutors (r s (133) = 0.22, p  = 0.02) are positively correlated with academic performance.

Conclusions

Social network analysis is a practical method that can reliably monitor the interactions in an online PBL environment. Using SNA could reveal important information about the course, the group, and individual students. The insights generated by SNA may be useful in the context of learning analytics to help monitor students’ activity.

Peer Review reports

Social constructivists view learning as an active construction of knowledge that occurs through social interaction and dialogue among learners. Interaction becomes particularly meaningful when learners are coached in a positive atmosphere and when the students have the chance to argue, debate, and offer alternate perspectives or contribute to ideas [ 1 , 2 ]. Aiming to harness the potentials of the approach, the social constructivist methods have been embraced by several modern pedagogies such as problem and team-based learning that encourage collaboration and promote meaningful interactions among learners. Problem-based learning (PBL) uses problems as triggers to facilitate discourse and interaction among students, the discussions occur in small groups and are facilitated by teachers [ 3 ]. The PBL process is structured in a way to help students elaborate and activate previous information [ 3 , 4 ].

Online problem-based learning requires students to engage in active discussions in two types of dialogical spaces; the content and the relational spaces. Learners interact in the content space towards the goal of acquiring a deeper understanding of the domain of knowledge; activities include collecting information, discussing concepts and proposing solutions to the problem [ 5 , 6 , 7 ]. Communicative activities in the relational space deal with the interpersonal relations and interactions among collaborators. The primary goal of these interactions is to reach a common understanding of the concepts and content under discussion [ 5 , 8 ].

There is overwhelming evidence of the value of learners’ interaction with peers, teachers, and the content, which is widely recognized as a pivotal ingredient of modern education [ 9 , 10 ]. However, offering the learners the opportunity to interact does not directly translate to effective interactions and working online together does not mean collaboration [ 6 , 11 , 12 ]. Moreover, online collaboration requires instructional support, scaffolding by teachers, active coordinating of collaboration dynamics, engagement of learners in a stimulating environment [ 11 , 13 ], which begs the need for a mechanism to monitor the efficiency of online interactions and design a data-driven intervention that supports effective collaboration.

Social network analysis

Social network analysis (SNA) is a collection of methods and tools that could be used to study the relationships, interactions and communications. As such, SNA is suitable for the study and possibly monitoring of online interactions as it can automatically analyze interaction data, bringing a bird-eye view of the group social structure, the interaction patterns, as well as the mapping of all communications in the relational space [ 14 , 15 , 16 , 17 ]. SNA can be implemented in two main ways, visualization, and mathematical analysis.

SNA visualization renders relationships between actors in social networks by graphs known as sociograms; the sociogram portrays actors (nodes in SNA terms) as points, and relationships (edges in SNA terms) as arrows originating from the source of the interaction and pointing to the target of interaction [ 17 ]. SNA may help visualize the interactions among participants and may reveal who are the important actors in the interactions and who are the isolated actors, what are the groups that shows dense interactions or sparse interactions that may need support. It may also reveal the active moderators who are participating and interacting with students, and the extent of their interactions. PBL is a collaborative learning where small groups interdependently work together and as such evaluation of collaborative interactions is important.

The SNA mathematical analysis quantifies network parameters on individual actor levels, as well as group level. The mathematical analysis of SNA uses graph theory concepts to calculate metrics representing the nodes, the links or the network. Such as the distance to other actors in the network, the number of interactions with other actors, or how many times it bridged interactions between communities. These metrics are important in quantifying interactions, ranking nodes, or relationships. Parameters calculated at the actor level are called centrality scores, these scores are measures of the node position or importance in the social network. Since there are different contexts and probably different ways to consider the role important, there are different centrality scores. The measures chosen for this study are centrality measures of role and position in information exchange [ 17 ]. For example: number of received interactions are quantified as in-degree centrality, number of contributed interactions is called out-degree centrality. These parameters can add to our portfolio of information about students and might extend to learning analytics [ 14 , 15 ]. In the context of PBL, one would be interested in the interactivity of participants, and their groups, and how these metrics could tell us about online PBL.

Previous research has indicated the utility of SNA in the study of collaborative learning; J Zhang and J Zhang [ 18 ] used SNA to diagnose gaps in problem solving and knowledge construction. Their findings highlighted problems such as students with low levels of interaction, the presence of few central students to the exchange of information. A Bakharia and S Dawson [ 14 ] have shown how to use SNA to map the online interactions among groups of students and identify the communities of collaborators. They were also able to recognize influential and isolated students who might need to be stimulated or supported. M Saqr, U Fors and M Tedre [ 19 ] used social network analysis to monitor collaborative groups and create a data-driven intervention that results in a meaningful improvement in the way students collaborate. Previous research results in learning analytics are promising, and some studies have given an indication of the potential of SNA centrality measures in predicting students’ performance [ 20 , 21 ].

There is an apparent gap of the studies about the interactions in online PBL, particularly the relational and communicative activities. Most studies about the interactions in PBL targeted the content dimension through effortful resource intensive methods such as interviewing the learners, recording, and coding of PBL sessions or text mining techniques. Other researchers have used indirect examination and exploration using surveys and open-ended questionnaires [ 7 , 22 ]. Such methods are practically demanding as well as challenging to standardize and difficult to implement by instructors [ 23 , 24 ]. It is reasonable to argue that there is a need for mechanisms to ensure that online PBL meets the required curricular goals and objective and is actually collaborative. The methods need to be automatic, easy to implement and offer meaningful information that can help stakeholders improve the PBL process. In this study, we investigate the use the potentials of SNA in supporting learning and teaching. We have considered both visualization and mathematical analysis. Since the PBL process has three important aspects: the student, the actor and the group, we have used these aspects as the units of analysis.

The research question of this study can be formulated as follows:

What can SNA visual and mathematical analysis tell about PBL?

What interaction parameters as measured by SNA associated with better performance?

The study was designed as an exploratory case study; a case study design allows an in-depth investigation of a case (Online PBL course in our study) from different perspectives.

Participants

The study included data from 135 first year students (42 females and 93 males) and 15 tutors (10 males and 5 females), three students did not sign the ethical approval and 11 students dropped out of the course and so their data were not included. The course chosen was the Growth and Development of the year 2017, attended by first year students, the course is the third course in the year. The course covers issues of growth and development from different aspects, including anatomy, physiology and embryology. By the third course, students should have acquired a fair experience of the educational system and practiced online PBL for three months. The course duration is around 45 days, within them four weeks of PBL sessions were completed by all students.

Qassim College of Medicine, Saudi Arabia has introduced PBL since 2001. The college adopted the seven jumps approach, student is expected to attend two sessions physically, one at the beginning of the week and another at the end, in between these sessions, students are offered an online forum where they continue to interact. The interactions start on the first day of the week by posting the learning issues of the weekly problem. Students are encouraged to discuss the learning issues, share information, collaboratively construct the required knowledge and work towards achieving the goals and the objectives of the PBL problem. By the last day of the week, each group is expected to wrap up what they have learnt together. The online discussions are assigned to the same students in the physical group and facilitated by the same tutors [ 7 ].

The PBL represents the backbone of the educational system at the medical college and the objectives of the PBL sessions are written to cover the course objectives. The lectures, seminars and other educational activities are designed to support students understand the problem used in the PBL. The students are evaluated by multiple choice questions (MCQ), modified essay questions (MEQs) and Short-answer Essay Questions (SEQ) as well as practical exams. The grades considered for this study are only the sum of MCQ, MEQ and SEQ as they are designed to test students’ knowledge acquisition of PBL objectives and the exam blueprint is designed according to these objectives. The mean difficulty index of MCQs was 0.67, the mean discrimination index was 0.32 and the reliability index was 89.2%.

Data collection

Interaction data were extracted from the learning management system. Custom Structured Query Language (SQL) queries were used to extract interaction data along with attributes of students and properties of posts, the data extracted for each post were the id of the sender, the id of the receiver, time the post was written, time it was modified, the content of the post, and forum and thread IDs. For each user we extracted user Id, role, PBL group and the posts he contributed. All participant IDs were removed to protect their confidentiality and re-coded, so the students labeled as S1 to S135 and the tutors were recoded as T1 to T15.

SNA analysis

The analysis of SNA was done using Gephi version 9.2. Gephi is an open source application that has the capability to visually and mathematically analyze social networks through a graphical user interface. It can import a range of formats as well as export analysis data in an easy to use format for analysis [ 25 ].

SNA visual analysis

The course network, as well as group networks, were analyzed and studied visually. Visualization was implemented to describe the general properties of the course network, such as the interactivity level, participants’ contributions, who is participating and identify the types of communications. The same was performed for each PBL group with special emphasis on the student-student and student tutor communications.

SNA mathematical analysis

Mathematical analysis of participants’ interactivity may shed lights on important aspects such as interactivity and information exchange. As such, mathematical analysis was done at two levels, Individual students’ level (student and tutor), and group level, since they are the important factors in the PBL process.

Student level

For each student, we collected the following parameters: Type of interactions (student-student, student-tutor, and tutor-tutor). We also calculated centrality measures that are relevant to interactions and role in information exchange:

Indegree centrality: the number of posts that are directed to a user, such as replies. A user receives more replies to contribution if the contribution is noteworthy, useful or arguable. As such in-degree centrality in this context reflect the importance of a user contribution as voted by other users. Out-degree centrality: the frequency of posts made by the user and reflects the activity of the user in the discussions.

Degree centrality: is the sum of indegree and outdegree centralities and reflects the overall activity of the user

Betweenness centrality: is the frequency a user connected two other unconnected users, or lied in-between their interactions. As such, it may be viewed as a measure of bridging others and helping communications among interacting participants.

Closeness centrality: reflects the distance between a user and all other users in the group, a user with high closeness centrality can be easily reachable by all others.

As this article is beyond the discussion of the theory, mathematics and concepts of centrality measures, interested readers can have further details in this article [ 26 ].

Group level

For each group, we collected the following parameters: The total number of interactions and type (student-student, student-tutor, and tutor-tutor), average in-degree centrality (average in-degree of all group members), average degree centrality (average degree of group members).

Statistical analysis

Statistics were performed using Paleontological Statistics Software Package for Education and Data Analysis version 3.2. Correlation among variables was performed using the non-parametric Spearman rank correlation test [ 27 ].

Descriptive statistics

The course included 15 PBL groups; each group had a range of 6 to 12 students with one tutor. There were six discussion forums: five PBL discussion forums and a news forum (for course announcements, data of the news forum were excluded from the study. The course had 2620 interactions, mostly from students to students (89%), students to teacher interactions were 4.9% and teacher to student interactions were 6.15%. The range of interactions in each group was 48 to 332 (mean 216, SD 81.5). The picture was also similar on the course level; students dominated the interactions. Some groups were highly interactive such as groups 150, 144 and 149 and some were not such as groups 146, 140 and 141. A breakdown of each interaction type is presented in the course is presented in Table 1 for group level.

RQ1: what can SNA visual and mathematical analysis tell about PBL?

SNA can be used to map the relationships among actors, groups (visual analysis) and calculate the mathematical metrics of interactions and actors. The visual analysis was used to explore its role in shedding lights on the individual actors and their groups and how could this help learners or teachers. Furthermore, the mathematical analysis was used to quantify the interactions on the same levels, to demonstrate the utility of quantifications of interactions and how it compares to visual analysis.

Visual analysis

SNA visual analysis can be used on different levels, in our case three levels are relevant: the whole course level, the PBL group level and individuals’ level. Following is a demonstration of what each level can reveal visually.

On course level

One of SNA biggest advantages is the ability to sum up and map all interactions, as such it offers a bird-eye view of the relational dynamics in a course or a group. In the next Fig. 1 , SNA rendered each PBL separately and identified each group, a property known as finding communities . This property is useful in finding the group of members who frequently interact with each other. The Figure shows each PBL group and the interactions among participants. Active groups can be recognized; an example is group A, B, and C; the three groups have dense interactions among participants. Groups D, E, and F, have a moderate level of activity; while G and H groups are less active with very few interactions among participants. Using such method may help pick the groups that need attention, a focused review or support.

Interactions in all groups. Each circle represents an actor, each arrow represents an interaction, groups were coloured with unique colours to be easy to distinguish

On group level

While the insights from the whole course network can be helpful to have a course overview, a focus on each group separately can demonstrate deeper information. In each group, the visual analysis using SNA can reveal the patterns of the interactions occurring in the group and how the students and the tutor perform; which are relevant factors for the evaluation of the PBL process. To demonstrate the potential of SNA and how much insight it can give, we present three Figures, in Fig. 2 , group (A) is visually rendered in a sociogram. Regarding participants’ roles, it can be noted that student S83, S84, S15 are the most active students, while S110, S98 are the least active and are relatively isolated. The interactions are mostly occurring among students S15, S101, S84, S106 and the tutor. The role of the tutor who is coded as T150 can also be recognized, the node size indicates a moderate level of activity, and the arrows directions are indications that he received many interactions as well as communicated back with most students, the dark node color is an indication that his role was mostly moderating discussions among students. It is reasonable to say here that the information about the tutor was assuring that that tutor acted as expected from him. Regarding the group, the multiple interactions are an indication of an active group with diverse participation among most participants. To summarize, the picture SNA is rendered in Fig. 2 , a picture of an active group with many active students, and few inactive students S110 who might need more attention from the tutor.

Interactions in group A. The node size was configured as degree centrality to reflect participants’ activity. The edges were configured so that the more frequent the interactions between two participants are, the thicker the line will be. The colour intensity reflects role in moderating interactions

In contrast to the previous group, the next group (I) shows far lower level of activity. However, as shown in Fig. 3 , student S5 is the most active participant, less so are students s58, S61 and S68, the remaining students are mostly inactive, the tutor role was very limited and made a single interaction with S5. Apparently, such group requires attention and the tutor might be contacted to play a more active role in the group, to stimulate students and promote interactions.

Interactions in group I. The node size was configured as degree centrality to reflect participants’ activity. The edges were configured so that the more frequent the interactions between two participants are, the thicker the line will be. The colour intensity reflects role in moderating interactions

On individual level

On the individual level, SNA can highlight the network of individual users. For instance, we can have a student of interest that we would like to see his performance. For that situation, we can isolate his network in what is known as an ego network. To demonstrate this, we demonstrate the ego network of the tutor in another group, namely the ego network of the tutor of group I in Fig. 4 . Although the group included 11 students, the tutor had only five students who communicated with him. A look at the full network in the side image, we can see that the students who needed help the most (the less active) were not helped, this is a clear example of an opportunity where an improvement can be made, by alerting the tutor to diversify his interactions and focus on the isolated students.

The tutor ego network compared to the whole network showing the tutor has interacted with a limited number of students. The node size was configured as degree centrality to reflect participants’ activity. The edges were configured so that the more frequent the interactions between two participants are, the thicker the line will be. The colour intensity reflects role in moderating interactions

Mathematical analysis

On the course level.

Mathematical analysis offers a quantification of interactions and roles which may enable accurate comparison of students and groups. It is also potentially useful as a measurement that can be used to forecast performance. The descriptive properties of participants in our study were as follows: the average outdegree centrality was 17.47, which means each participant has contributed with an average of 3.5 posts/week during the five weeks duration of the course, which is an indication of a relatively interactive course. The centrality measures of information exchange were also acceptable, the closeness centrality normalized score was 0.66, meaning that most students had moderate to high reachability. Same parameters can be obtained for the group and they are presentenced in the next section.

Further details are in Table 2 .

On the group and individual level

As a demonstration, the mathematical parameters of a student are summarized in Fig.  5 , compared to own group. The student, were actively contributing (outdegree 50) which is average 10 posts every week, received 27 replies (indegree 27), an average of 5.4 posts/week, an indication of the interest of peers to reply to the posts. Furthermore, closeness centrality is high which means he was close in the discussions to all other collaborators. However, betweenness 28.8 centrality is high, an indication that the student has connected and bridged interactions with others. The parameters of the group were also listed on the side to see how the student compared to others in the same group. The parameters were relatively high (high indegree, high outdegree, high closeness and betweenness centralities). One can reach the conclusion that that this was an active student in an active group.

A social network profile model

Social profile

To sum up a student or a user activity, we propose using SNA as a monitoring tool that displays the activity of a use (visual and mathematical) compared to own group for the teachers to use. The social network profile concept we propose can be applied in a learning management system dashboard to show social profile, connections and parameters. To demonstrate this, we chose a student from group A, showing ego network, the activity level and parameters for clarity, we also compare the activity to the group side by side. Comparison to his group will show if his activity level is proportional to colleagues or not and will highlight the other participants’ role. In case there is an unsatisfactory level of activity, one can understand the reason.

RQ2 do interaction parameters - as measured by SNA - correlate with better performance?

To see which of the SNA factors might correlate with better achievement, we categorized the parameters into three main groups: personal, group and tutor factors. The results indicated that outdegree centrality of a student, being in a group with higher average grade, communicating with the tutors, are the factors most correlated with better learning. The full details of correlation coefficient are displayed in Tables 3 .

This study was done to evaluate the role SNA can be used to evaluate online PBL and offer insights to administrators, tutors, and students. We have looked at the visual analysis and the quantitative interaction statistics. The type of interaction based on the involved participant whether he is a tutor or a student was considered. We also used role classification to try to understand the dynamics of intragroup interactions.

On the quantitative level, SNA interaction analysis offered simple, easy to interpret indicators for the level of activity of students and tutors; these indicators helped identify the active groups, tutors, and students alike. The indicators also helped flag a potentially dysfunctional or inactive group and the inactive student or a disengaged tutor. The SNA measures of role and position in information transfer highlighted qualities that can’t be easily recognized by the interaction parameters (number of posts). For instance, betweenness centrality reflected the role of students in moderating information, connecting the unconnected students and brings them into the discussion. Closeness centrality pointed to students who were not easily engaged in the discussions.

When coupled with visual analysis, it gave further insights into the dynamics of the interactions in the groups, such as who was active, who were the students interacting together and highlighted the isolated students. The role analysis we have performed in our study added another dimension to the utility of SNA. Identifying each role and who plays it enables tutors to understand dynamics in the group, helps motivate discussions and mitigate conflict or prevent dysfunction in interacting groups as well as appreciate different points of view [ 28 , 29 ].

Studies of interaction analysis in online PBL are scarce, our review of the literature identified one study about the analysis of problem-solving online discussions, which is apparently not precisely online PBL [ 30 , 31 ]. Another study by Saqr et al. that focused on using social network metrics as proxy leaning analytics indicators to predict performance using machine learning methods and advanced modelling techniques. The importance of our study is that it offers a practical monitoring solution that can be implemented and interpreted by different stakeholders, the insights generated have the potential to improve many aspects of the online PBL, and at times help intervention in a dysfunctional group or a disengaged student [ 32 ]. SNA based intervention has proved practical and effective in bringing tangible results that could support teaching and learning in other settings and such methods are similarly applicable in PBL [ 32 ].

While content analysis might have added insights about the exchanged information in online PBL, it is resource intensive and requires long, exhaustive work. Since content analysis is usually done manually, it is not a practical choice for automatic monitoring of thousands of interactions. Content analysis has also been criticized for being subjective and difficult to standardize [ 33 , 34 , 35 ]. Educational data mining (EDM) offers a potential alternative as it can automatically analyze text in the real-time. Nonetheless, EDM is impractical beyond research purposes since the instruments and frameworks used are difficult to use by teachers. The most challenging hindrance we found in the context of online PBL was the absence of context-specific tools since most of the available tools are from outside the field of education, a problem Alejandro [ 36 ] stated as “Many of the EDM researchers are in reality users of Data mining frameworks and tools. Because student modeling commands the focus of more than half the approaches.”

While our research was feasible due to the availability of data recorded by the learning management system and the relative ease of obtaining it, a similar approach can be extended to include the face-to-face PBL. The modern voice recognition techniques can accomplish it, since transcribing tools that are becoming more accurate, cheaper and easier to implement. The implementation would require more discussion about what to transcribe, what to store and how to handle this data.

We think that our study offered a demonstration of the potentials of SNA as a tool for interaction analysis of online PBL. We hope that this study can kindle the discussions about the need for automatic monitoring of PBL. We hope that future research can examine, improve or critique our approach. An obvious future approach to online PBL could couple learning analytics methods with SNA methods.

While this study has shed lights on the communicative and relational aspects of the online PBL, it is not without limitations. We recognize the limitation and the need for a reliable content analysis method. An automatic EDM method that is designed to capture the PBL elements and give a credible insight into the content of the discussions. We also recognize that our results might need further confirmation in other contexts to advance our understanding of the process, especially different implementations of online PBL.

Social network analysis is a practical method that can reliably monitor the interactions in an online PBL environment. Using SNA can reveal important information about the course, such as the general activity and the active groups. On the group level, it renders a more detailed picture about the group and the participants. Thus, helping to identify the active, inactive or isolated students and tutors alike. Using mathematical parameters can also help add insights about the level of activity in a precise way. Furthermore, the insights generated by SNA can be useful in the context of learning analytics to help monitor students’ activity.

Abbreviations

Educational Data mining

Multiple Choice Questions

Modified Essay Questions

Problem Based Learning

Research Question

Standard Deviation

Short-answer Essay Questions

Social Network Analysis

Structured Query Language

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Saqr, M., Alamro, A. The role of social network analysis as a learning analytics tool in online problem based learning. BMC Med Educ 19 , 160 (2019). https://doi.org/10.1186/s12909-019-1599-6

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A Systematic Review on Social Network Analysis: Tools, Algorithms and Frameworks

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ICCBD '18: Proceedings of the 2018 International Conference on Computing and Big Data

In the modern paradigm of IOT "Internet of Things" and big data, we deal with augmented volume of information on regular basis. Due to advancement in the data flow, there is a fair chance of data loss and its misuse. To manage this, it is usually required to draw whole information in form of graphs or presentable form which is termed as social network. In this context, to get some calculative idea about social relations from that network is called a Social Network Analysis (SNA). The research on SNA has been continuously performed from last decade or so. It is required to review and present major contributions in SNA to research community. Therefore, this article conduct a Systematic Literature Review (SLR) to select fifty two research studies. As a result, twenty three algorithms, eleven tools and ten frameworks in the domain of SNA have been presented. This facilitates researchers to discovery latest SNA developments within a single study.

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Thematic series on Social Network Analysis and Mining

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Social networks were first investigated in social, educational and business areas. Academic interest in this field though has been growing since the mid twentieth century, given the increasing interaction among people, data dissemination and exchange of information. As such, the development and evaluation of new techniques for social network analysis and mining (SNAM) is a current key research area for Internet services and applications. Key topics include contextualized analysis of social and information networks, crowdsourcing and crowdfunding, economics in networks, extraction and treatment of social data, mining techniques, modeling of user behavior and social networks, and software ecosystems. These topics have important areas of application in a wide range of fields, such as academia, politics, security, business, marketing, and science.

1 Introduction

This Thematic Series of the Journal of Internet Services and Applications (JISA) presents a collection of articles around the topic of Social Network Analysis and Mining (SNAM). From advances in Computer Science research and practice, the field of SNAM has become an important subject due to (i) the large amount and diversity of data that could be analyzed, (ii) the capacity of processing and solving complex analysis with efficiency, (iii) the development of new solutions for visualization of complex networks, and (iv) the application of SNAM concepts in different domains.

The study of social networks was leveraged by the social, educational and business communities. Academic interest in this field has been growing since the mid twentieth century [ 1 ], given the increasing interaction among people, data dissemination and exchange of information. In this scenario, big data sets require more accurate analyses. As such, the development and evaluation of new techniques for social network analysis and mining (SNAM) is a current key research area for Internet services and applications. These topics have important areas of application in a wide range of fields.

A social network is composed of actors who have relationships with each other. Networks can have a few to many actors (nodes) and one or many types of relationships (arrows) between pairs of actors [ 2 ]. In our daily life, we have several practical examples of social networks: our family, friends, and colleagues from the university, gym, work, or casual meetings. Individuals and organizations – seen as nodes in social networks – can be connected due to several reasons, such as friendship and genealogy, but also values, visions, ideas, finances, disagreements, conflicts, services, computer networks, air routes etc. The structure created from such a large amount of relationships is complex. Therefore, researchers study the network as a whole from a sociocentric view (all the links referring to specific relations in a given population), or as a social structure in an egocentric view (with links selected from specific people) [ 3 ].

In addition, people join and create groups in any society [ 4 ], but the web platform fostered critical changes in the way people can interact and think about the reality. Interactions (i) become easier, (ii) allow a frequent exchange of information, and (iii) transform communications tools and social media (e.g., microblogs, blogs, wikis, Facebook) to mass communication means that are more agile and far-reaching. As such, the use of social media contributes to the sharing of different types of information, especially in real time. Some examples are personal data, location, opinions and preferences. In this context, SNAM can support the understanding of preferences and associations, the identification of interactions, the recognition of influences, and the comprehension of information flow (context and concepts) among network actors.

Finally, the understanding of interactions in a specific scenario can produce concrete results. In an organization, employees should work to avoid problems regarding knowledge sharing [ 5 ]. In natural science, social networks can aid in the study of endemies and epidemies propagation [ 6 ]. In marketing, SNAM can be used as a tool for brand spread, or for the study of a market segment towards the understanding of how information propagates [ 7 ]. The last (but not least) example is the use of SNAM for the identification of criminal networks [ 8 ].

This JISA Thematic Series originates from the 6th Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2017) that was held in São Paulo, Brazil, on July 04–05, 2017. BraSNAM 2017 was affiliated with the 37th Brazilian Computer Society Congress (CSBC 2017) which is the official event of the Brazilian Computer Society (SBC). BraSNAM is focused on bringing together researchers and professionals interested in social networks and related fields. The workshop aims at providing innovative contributions to the research, development and evaluation of novel techniques for SNAM and applications. Finally, the main goal is to provide a valuable opportunity for multidisciplinary groups to meet and engage in discussions on SNAM.

Continuing in this direction, this JISA Thematic Series targets new techniques for the field of SNAM, mainly fostered by the context of Internet services and applications. We received contributions at various levels: from theoretical foundations to experiments and case studies based on real cases and applications; from modeling to mining and analysis of big data sets; and from different subjects and domains, such as entertainment, public transportation, elections, and personal social circles.

This Thematic Series presents high-quality research and technical contributions. We received six submissions as extended versions of the best papers of BraSNAM 2017. Topics included: analysis of online discussion and comments, complex networks, graph mining, government open data, power metrics, community detection, link assessment, homophily, and sentiment analysis. The five out of six submissions that were selected for publication and appear in this issue are summarized in the following section.

2 The papers

Loures et al. [ 9 ] investigate the potential that online comments have to describe television series. The authors implement and evaluated several different summarization methods. Their results reveal that a small set of comments can help to describe the corresponding episodes and, when taken together, the series as a whole.

Caminha et al. [ 10 ] use graph mining techniques for the detection of overcrowding and waste of resources in public transport. The authors propose a new data processing methodology for the evaluation of collective transportation systems. The results show that their approach is capable of identifying global imbalances in the system based on an evaluation of the weight distributions of the edges of the supply and demand networks.

Verona et al. [ 11 ] propose metrics for power analysis on political and economic networks based on a sociology theory and network topology. The authors present a case study using a network built on data from Brazilian Elections about electoral donations explaining how the metrics can help in the analysis of power and influence of the different actors (corporations and persons) in this network.

Leão et al. [ 12 ] propose a method to handle social network data that exploits temporal features to improve the detection of communities by existing algorithms. By removing random relationships, the authors observe that social networks converge to a topology with more pure social relationships and better quality community structures.

Finally, Caetano et al. [ 13 ] propose an analysis of political homophily among Twitter users during the 2016 US presidential election. Their results showed that the homophily level increases when there are reciprocal connections, similar speeches or multiplex connections.

3 Paper selection process

The paper selection process was run during 2018 and the papers were published as soon as they were accepted and online-first versions became ready. Each submission went through two to four revisions before the final decision. We invited leading experts who are international researchers in the field of SNAM and related topics to form this Thematic Series’ editorial committee. All manuscripts were reviewed by at least three members of this editorial committee. Guest editors checked the new version produced after each review cycle in order to decide whether the authors carefully addressed the reviewers’ comments. Otherwise, a further review cycle was requested by the guest editors. The papers were reviewed by a total of 19 reviewers. The names of the editorial committee members are listed on the acknowledgements of this editorial.

4 Conclusion

The future of research and practice in the field of SNAM is challenging. Opportunities are many: theoretical and applied research has been published in specific conferences and journals, but also in traditional venues since it requires a multidisciplinary arrangement. Based on the papers accepted to this Thematic Series, we can highlight some research gaps. For example, Loures et al. [ 9 ] point out the challenge of abstractive summarization for online comments : it is usually a much more complex task than the extractive one, since it requires a natural language generation module and a domain dependent component to process and rank the extracted knowledge. In turn, Caminha et al. [ 10 ] point out the need for a simulator for reproducing the dynamics of human mobility through the bus system in the case of a large metropolis. In this context, the use of data mining to estimate probability can represent the current demand for a bus system.

Regarding applications of SNAM in the context of presidential elections, Verona et al. [ 11 ] point out the challenge of redesigning the power metric to show relative values inside the network, instead of big absolute values. Moreover, information about company owners should be integrated in order to reveal hidden connections behind donations and politicians. In turn, Caetano et al. [ 13 ] point out the need for further investigation on the temporal political homophily analysis correlating it with external events that may have influenced the users’ sentiments. This effort can allow user classification through data mining techniques to identify candidates’ advocates, political bots, and other actors. Finally, regarding community detection, Leão et al. [ 12 ] point out the challenge of adopting different approaches for community detection , consider additional algorithms to explore temporal aspects or identify overlapping communities, and evaluate filtered networks. Moreover, different alternatives to measure the strength of ties should be investigated.

In the end, this Thematic Series comes out with some meaningful over-arching results:

SNAM researchers and practitioners recognize the importance of sentiment analysis for in the identification of conflicts and agreements, as well as social trends and movements, in different domains. As such, new methods and techniques should be developed based on the large set of existing empirical studies on this topic;

The dynamic nature of social networks makes community detection somehow a hard work. Different algorithms exist and are many. However, the treatment of randomness and noise in social relations requires further investigation. In addition, the assessment of those relations over time is also a topic of interested in SNAM;

Another challenge in the area is the understanding of social power and the way it manifests in social networks. In this context, power is tightly related to the notion of influence and authority. Research can vary from the development and use of SNAM algorithms and tools to the theorization based on qualitative studies (e.g., case studies, ethnography, sociotechnical approaches);

SNAM opens opportunities to investigate different types of systems, such as (i) systems-of-systems: a set of constituent software-intensive systems that are managerially and operationally independent, and present some emergent behavior and evolutionary development (e.g., smart cities, transportation, air space, flood monitoring), and (ii) software ecosystems: a set of actors and artifacts as well as their relations over a common technological platform (e.g., iOS, Android, Eclipse, SAP);

Finally, SNAM can support research on new trends of collaborative systems, such as crowdsourcing, free and open source software development, accountability, transparency and community engagement. A common interest lies on how to improve information visualization and recommendation based on actors’ characteristics and behaviors as well as the changes in their relations over time.

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Acknowledgments

We thank all the authors, reviewers, editors-in-chief, and staff for the great work, which supported this Thematic Series on a very important topic both for the research community and for the industry. In particular, we thank all the editorial committee members: Alessandro Rozza ( lastminute.com Group, ITALY), Altigran Soares da Silva (Federal University of Minas Gerais, BRAZIL), Antonio Loureiro (Federal University of Minas Gerais, BRAZIL), Ari-Veikko Anttiroiko (Tampere University), Artur Ziviani (National Laboratory for Scientific Computing, BRAZIL), Bernardo Pereira Nunes (Pontifical Catholic University of Rio de Janeiro, BRAZIL), Claudio Miceli de Farias (Federal University of Rio de Janeiro, BRAZIL), Daniel Batista (University of São Paulo, BRAZIL), Flavia Bernardini (Federal Fluminense University, BRAZIL), Giacomo Livan (University College London, UK), Isabela Gasparini (Santa Catarina State University, BRAZIL), Jesús Mena-Chalco (Federal University of ABC, BRAZIL), Jonice Oliveira (Federal University of Rio de Janeiro, BRAZIL), Leandro Augusto Silva (Mackenzie Presbyterian University, BRAZIL), Luciano Antonio Digiampietri (University of São Paulo, BRAZIL), Luiz André Portes Paes Leme (Federal Fluminense University, BRAZIL), Mirella Moro (Federal University of Minas Gerais, BRAZIL), Raimundo Moura (Federal University of Piauí, BRAZIL), Yosh Halberstam (University of Toronto, CANADA). The voluntary work of these researchers was crucial for this Thematic Series.

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Social network analysis in innovation research: using a mixed methods approach to analyze social innovations

• Nina Kolleck 1  

European Journal of Futures Research volume  1 , Article number:  25 ( 2013 ) Cite this article

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The importance of social networks for innovation diffusion and processes of social change is widely recognized in many areas of practice and scientific disciplines. Social networks have the potential to influence learning processes, provide opportunities for problem-solving, and establish new ideas. Thus, they can foster synergy effects, bring together key resources such as know-how of participating actors, and promote innovation diffusion. There is wide agreement regarding the usefulness of empirical methods of Social Network Analysis (SNA) for innovation and futures research. Even so, studies that show the chances of implementing SNA in these fields are still missing. This contribution addresses the research gap by exploring the opportunities of a mixed methods SNA approach for innovation research. It introduces empirical results of the author’s own quantitative and qualitative investigations that concentrate on five different innovation networks in the field of Education for Sustainable Development.

Introduction

Scholars interested in innovation processes and futures research have often stressed the importance of social networks. Social networks are seen as an important factor in how ideas, norms, and innovations are realized. Social network research understands individuals within their social context, acknowledging the influence of relationships with others on one’s behavior. Hence, social networks can promote innovation processes and expand opportunities for learning. Despite the consensus regarding the value of social network approaches, there is a lack of empirical investigations in innovation and futures studies that use Social Network Analysis (SNA). In most cases, the scientific literature uses the concept of social networks metaphorically, ignoring the chances presented by SNA methods. At the same time, conventional empirical research in innovation and futures studies often disregards relational information. Hence, analyses of statistical data on structural and individual levels are treated as separately. Activities that are expected to have impacts on future developments are usually modeled as isolated individual or group behavior, on the one hand, or as the characteristics of structural issues, on the other hand. SNA provides us with empirical tools that capture the social context and help to better understand how innovations are implemented and diffused and why social change takes place. Network approaches explicitly challenge the difference between deduction and induction and highlight the relevance of relationships. Individuals both shape and are shaped by the social context in which they interact. By applying techniques of SNA, actor-centered and structuralist reductions are avoided. Instead, SNA emphasizes the mutual influence of structure and social connections. In order to better understand and model developments in innovation and futures research, relational data inherent to the social network perspective is needed.

This contribution addresses the opportunities of SNA for innovation research. It is divided into six sections. After this introduction , the second section briefly defines crucial concepts of SNA and provides theoretical background. The third section discusses the value of a social network perspective for innovation research. The methodological approach, along with the empirical case studies used, is outlined in the fourth section. The fifth section shows how a combination of both insights from structure based on quantitative SNA and subjective perceptions revealed with qualitative SNA is helpful for understanding innovation processes. Here, the integration of qualitative SNA such as egocentric network maps in quantitative techniques of SNA is illustrated. The contribution concludes with a summary of main arguments.

Theoretical and methodological background

While in the scientific literature there are diverse understandings on what a social network is, this contribution draws on the definition used by Stanley Wassermann and Katherine Faust:

“A social network consists of a finite set or sets of actors and the relation or relations defined on them. The presence of relational information is a critical and defining feature of a social network” [ 1 ].

This conception of social network permits both a governance approach and empirical techniques of SNA. Scholars of governance research understand social networks as a certain type of governance that can be differentiated from other ideal types of governance: markets and hierarchies. Social networks combine market-based and hierarchic dimensions and serve as a form of hybrid governance [ 2 ]. Both weak and strong modes of coordination are integrated into the network concept of governance research, where strong coordination is defined as “the spectrum of activity in which one party alters its own … strategies to accommodate the activity of others in pursuit of a similar goal” [ 3 ]. Weak coordination, on the other hand, takes place when actors observe each other’s behavior, “and then alter their actions to make their … strategies complementary with respect to a common goal” [ 3 ].

Because they promote constant exchange and deliberation, social networks have strong potential to promote ideological or structural changes and to generate new knowledge. Hence, network governance is not reduced to governmental action, but refers to the search for collective and participative problem-solving strategies and the promotion of innovations. Footnote 1 This article uses the concept of network governance to highlight the relevance of relationships for innovation research. Hence, it confronts the assumption that individual behavior is independent of any others, but instead conceives “problem-solving as a collaborative effort in which a network of actors, including both state and non-state organizations, play a part” [ 4 ]. Footnote 2

In order to better understand the opportunities of SNA for innovation research, this contribution introduces innovation networks in five different regions as case studies. Innovation networks are understood as social networks that aim at establishing a social innovation. Here, the social innovation of Education for Sustainable Development (ESD) is used. At the same time, the term social innovation refers to processes of implementing and diffusing new social concepts across different sectors of society. While “innovation” implies a kind of renewal, “social” connotes interaction of actors. Social innovations have a direct connection with the search for solutions to social problems and challenges [ 6 , 7 ]. Likewise, Education for Sustainable Development can be defined as education that empowers people to foresee, try to understand, and solve the problems that threaten life on our planet. With the goal of promoting behavioral changes that will shape a more sustainable future, ESD integrates principles of sustainable development into all aspects of education and learning.

Change and innovation through social relations

How can social networks evoke changes and what are the opportunities for SNA to promote innovation processes? SNA has the potential to overcome uncertainties related to innovation processes. The chance of an innovation gaining acceptance increase significantly if it is supported by interconnected actors rather than singular individuals. Social networks foster change processes and promote innovation diffusion. SNA techniques thus help to understand existing networks and to identify innovation potentials in order to generate new information and reveal options for structural developments. SNA has the capacity to promote innovation processes by dealing with the following issues:

Identification of innovation networks (existing, missing, possible, and realistic cooperation) and investigation of actors, structures, and network boundaries:

By using SNA methods, network structures were determined in previously defined fields. Thus, techniques of egocentric SNA provide us with necessary information with respect to network membership and structural interconnections between actors. Structural properties detected in the context of this project are, for example, centrality, prestige, or weak and strong ties.

Innovation potentials through network development strategies:

Looking at network structures not only fosters the development and diffusion of new ideas. It can also reveal where and how structural conditions enable innovations and development processes. Furthermore, Social Network Analyses disclose where and how cooperation can be optimized and where and how alterations are possible and reasonable. Presenting stakeholders the results of SNA can foster structural changes.

Identification and promotion of coordination, information, and motivation:

Analysis of social networks provides us with useful insights into knowledge transfer processes, showing where they exist and how “well” they function. Also, problems of coordination, information, and motivation become evident, providing us with knowledge related to development potentials.

Development of strategies to reduce uncertainties related to innovation processes:

The costs of information exchange are not only material (money, time), but also social. Uncertainties, lack of confidence, and the fear of a loss of reputation can prevent actors from sharing information and knowledge. Results of SNA help us to identify weaknesses in the knowledge transfer process.

Social network analysis in innovation research

In order to illustrate the key opportunities of SNA in innovation research, this section draws on the author’s own empirical investigations that used a mixed methods approach based on quantitative and qualitative SNA. Data on network members was drawn from five different German municipalities and included initiatives, institutions, thematic groups, and individuals engaged in the field of ESD. The municipalities studied are Alheim, Erfurt, Frankfurt am Main, Gelsenkirchen, and Minden. These municipalities have been awarded by the United Nations Decade of Education for Sustainable Development (UNDESD), 2005–2014, and are characterized by active networks in the field of the social innovation of ESD. Organizations, initiatives, and actors from different sectors of non-formal, informal, and formal education seek to further establish and diffuse the concept of ESD worldwide. Thus, networks within these municipalities can be regarded as best practices concerning their performance in the area of ESD. It should be taken into account, however, that the social networks analyzed here are neither institutionalized nor formally established organizations. Instead, every person engaged in the field of the social innovation is regarded as part of the network to be analyzed. Hence, defining the network boundaries was an important part of the empirical investigation.

The research design included three main steps. First, qualitative data was collected in order to gain a better understanding of the object of research and generate research hypotheses. Second, quantitative SNA was conducted, using both egocentric SNA and complete SNA techniques. Network membership and network boundaries were defined by mixed-mode egocentric SNA. In a first step, a 12-page questionnaire was sent to all persons in each of the five municipalities listed in the data base of the UNDESD. In a second step, all persons from different sectors named more than once were also approached with the questionnaire [ 8 – 10 ]. Referring to Fischer [ 11 ] and Burt [ 12 , 13 ], a name generator was used which allowed to name all relevant persons in the field of ESD. In this way, nodes were only included if they were mentioned more than once by an interviewee in the field of ESD.

The questionnaire first asked respondents to mark people in their ESD network, defined by efforts to contact, cooperation, collaboration, problem-solving, and idea exchange. Respondents were also asked to assess the quality and contact frequency for each relation mentioned and to name those persons with whom the interviewee cooperated especially closely or had established high levels of trust. They were then requested to score their named connections’ impact and the relevance with respect to the diffusion of information and the implementation of ESD. Finally, the questionnaire included questions on future prospects, desires, and developmental possibilities.

Egocentric network data was aggregated in order to enable applications of complete SNA. The (strictly adjusted) dataset of the whole network of all five municipalities consists of 1,306 persons and 2,195 edges. Subsequent to the quantitative studies, qualitative network maps were created in order to gain deeper insights into the qualitative characteristics of the networks’ structural properties. Footnote 3 This article focuses mainly on results from the second and the third part of the data analysis.

Insights from structures and individuals: engaging top-down and bottom-up approaches

Empirical results were visualized drawing back on UCINET, Netdraw, and Pajek in order to provide a comprehensive foundation for stakeholders [ 14 , 15 ]. Top-down visualizations of network data were used to generate courses of action, guidance, and network management strategies with the persons involved in the process. Thus, network visualizations and empirical insights enabled stakeholders to detect weaknesses related to structural issues, information flows, and communication problems.

In order to visualize the networks, directional relations between network members were entered into UCINET and mapped with Netdraw. The iterative method of “spring embedding” was chosen for the graph-theoretic layout, because it supports neat illustrations of data sets. Thus, the lengths of the ties do not have information content. The nodes in network visualizations represent persons engaged in implementing ESD in their municipalities. Against the backdrop of the definition of network boundaries, persons that are represented by nodes with only one ingoing link and no outgoing link were not interviewed.

To give an example, one surprising result was the low level of cooperation beyond municipal borders, as measured by network connections, as seen in Fig.  1 .

Trans-regional ESD network, generated with the graph theoretical layout spring embedding, source: Author’s data

In contrast to Manuel Castells [ 16 ], who observed a diminishing relevance of space due to the information age, the present study finds that space remains a constraint for diffusion of ESD. It seems much easier to establish the social innovation ESD in the local context with dense network structures and to subsequently foster its diffusion through weak ties [ 17 , 18 ].

Furthermore, municipal stakeholders were confronted with the unexpected existence of many structural holes and brokerage positions. The concepts “brokerage” and “structural hole” refer to actors’ structural embeddedness. A person who maintains connections with people, who do themselves not become interconnected, has the ability to mediate between these contacts and to obtain benefits from his brokerage position [ 19 – 21 ]. At the same time, structural holes impede innovation processes and information flows.

Figures  2 and 3 take Erfurt and Gelsenkirchen as examples and show relations regarding to the question of who is contacted to develop new ideas related to ESD. Only those relationships with a contact frequency of at least once a month are represented in this figure. The ESD network of Erfurt is chosen as an extreme example, because the structure of its social network exhibits the highest number of structural holes.

Cooperation in the development of new ideas in Erfurt, source: Author’s data

Cooperation in the development of new ideas in Gelsenkirchen, source: Author’s data

There are only a few network members engaged in developing new ideas with respect to ESD in Erfurt; many structural holes shape the ESD network.

In contrast, cooperation in the development of new ideas related to ESD works very well in Gelsenkirchen, as seen in Fig.  3 .

Figure  3 presents productive relationships in Gelsenkirchen. Gelsenkirchen was chosen as an example here because it demonstrates a nearly perfect cooperation basis, which is very supportive for successful innovation processes. Such results can be used by involved actors in order to disclose strengths and weaknesses and reveal where and how structural conditions enable innovations and development processes.

The network visualizations shown so far are mainly reduced to structural information. Network visualizations can also integrate further actor-related information. Not least, structural characteristics of social networks, processes of innovation, idea exchange, and trust also depend on the areas of activity to which network members belong. Thus, Fig.  4 integrates some actor-specific information. Nodes represent those people who are actively engaged in the field of ESD in Alheim. The color of the nodes indicates the sector in which the relevant person deals with ESD. The size of the nodes correlates with the individual centrality index. Centrality is measured by the frequency of the responses—the indegree [ 1 , 22 ]. The more often a person was identified by others, the more central she appears in the picture. The thickness of the connections varies depending on its individual clustering value. While there are two different measures (global and local) for clustering, the local version was used to give an indication of the embeddedness of single nodes [ 23 ]. Thus, clustering is defined as the number of common acquaintances; the thickness of the arrow connecting two nodes points to the number of triangular connections.

ESD network in Alheim; color of the nodes according to the area of activity ( blue black : non-formal education, red : administration/policy, yellow : NGOs, green : economy, light blue : formal education, orange : church, grey : other areas), numbers indicate the IDs of individuals, illustration in cooperation with fas.research, source: Author’s data

Figure  4 indicates the central role that people in the field of non-formal education play in Alheim, as measured by how often they were named by other people. Another central position is held by someone in government. The big red node has many incoming and outgoing links, but few triangle relations and thus a low clustering value. Further comparative quantitative studies reveal that despite its high density value, there is little clustering in Alheim. Certainly, the clustering value always depends on the data collection process, but as the study for this article has used the same methodological approach for all five municipal networks, it is possible to compare the municipal clustering values. However, the low clustering value in Alheim is because cooperation beyond institutional borders works very well in this municipality and persons are not always connected to the same partners. The fear expressed by other municipalities, that ESD in Alheim would be dominated by powerful politicians, cannot be confirmed from these results.

In general, quantitative SNA is able to highlight network boundaries and structural characteristics of social networks that are important to understand innovation potential and impediments. It is difficult or even impossible, however, to reveal the causes, motivations, ideas, or perceptions that lie behind such network structures by solely drawing back on quantitative SNA. How, for example, can we explain the central role of one politician in Alheim, while there are many other central persons from non-formal education? What role does this central politician play for the clustering value in Alheim? In order to answer these questions, the study had to draw on further qualitative social network research methods. The researchers thus used a combination of egocentric network maps and semi-structured interviews.

Egocentric network maps are more individual-oriented than quantitative SNA methods. One benefit of network map visualizations lies in their potential for mental or cognitive support. Such visualizations are able to promote subjective validations of interview narratives as well as to highlight subjective perceptions, reasons, motivations, and network dynamics. The technique of structured and standardized network maps, which has often been described as the “method of concentric circles” [ 24 ], was chosen for this study [ 25 ]. Here, network maps are not only aids, but a main purpose of the survey. A sheet with four concentric circles is given to the interviewee. The inner circle represents the ego, that is to say the interviewee. Interviewees are then asked to draw the initials of people important to them personally, differentiated by the degree of emotional proximity or contact frequency. The three circles around the ego represent the emotional closeness or formal distance with respect to her or his alters (or connections). The closer to the ego, the tighter a contact person is perceived by the interviewee. In addition, the circles are divided in parts through lines; each part represents a different area of activity. In this way, interviewees can dedicate their contacts to specific areas of activity, such as civil society, formal education, non-formal education, business or government. The space around ego is structured by both concentric circles that illustrate the closeness of the alters to ego and the area of activity in which alters are engaged for ESD. An essential advantage of the structured and standardized instruments in relation to unstructured techniques lies in the comparability between different network cards (both intrapersonal and interpersonal).

At the same time, the high degree of structuring and standardization constrains the significance of the data obtained. Indications beyond the pre-fixed circles are only possible if interviewers get the opportunity to pose further questions or if interviewees are encouraged to further discuss issues that are not explicitly part of the visualization process. In order to combine both standardization and openness, this study enabled the interviewers to pose further important questions and explore relevant information related to the research aims. The application of egocentric network maps also served as a medium through which interviewees talked about their relationships. In this sense, network maps were integrated into semi-structured interviews in order to generate narratives and disclose relevant relationships and action orientation. In addition, interviewees had the opportunity to choose the categories representing different areas of activity as well as the colors for the visualizations. Thus, the technique implemented in the study supported the comparability of the cases, but it was also open for new variables and dimensions related to the specific context.

Altogether 25 network maps and interviews, five in every municipality, were generated. Interviewees were chosen according to their area of activity (to obtain a variance of the cases), their position within the social network, and their centrality indexes. To give an example, Fig.  5 presents the network map of a central politician in Alheim. This network map of Alheim is also chosen to further illustrate the case of Alheim, which was also depicted in Fig.  4 . Furthermore, this ESD actor in Alheim possesses a high centrality value according to quantitative SNA.

Network Map of a central politician in Alheim, anonymized, source: Author’s data

As Fig.  5 shows, the interviewee mainly distinguishes five areas of activity: civil society, educational institutions, government/administration, business, and persons from trans-regional contexts. In some cases, the politician just wrote down an organization. During the interview, he referred to concrete persons from these organizations. Surprisingly, the sector of government/administration, to which the interviewee himself belongs, is empty: no persons or organizations are indicated. This is also reflected in the visualization based on quantitative network data (Fig.  4 ), where only one individual from government plays a central role. In a sense, qualitative studies validate quantitative results by showing that the social innovation ESD in Alheim is mainly implemented by actors from non-formal education. At the same time, qualitative results stress that the topic is supported and disseminated by one central politician who bridges structural holes between different sectors. Furthermore, school actors are not represented in the network map, whereas the closest contact persons are from civil society, educational institutions, and business. The great variety of close contact persons from different sectors can be regarded as one reason for the success of the social innovation in Alheim. The central politician in Alheim himself mentions this as playing a significant role. Further actors within the community stress that the ideological foundation and the adoption of ESD would not be possible without this politician. Hence, the establishment of ESD in Alheim can also be traced back to its structural and discursive power and the general trust of ESD actors in this well-connected politician.

The central role of the interviewee in Alheim can be ascribed to the fact that he bridges institutional clusters, supports cooperation beyond government/administration, and combines close cooperation with weak ties in the field of ESD. Furthermore, centrality is not reduced to one person or one sector. Instead, actors from different sectors play a central role in the field of ESD and cooperation between state and non-state actors is very high. In this way, it was possible to develop and realize aims in the area of political accountability in a short space of time. The dense network structure, supported by strong relations between one central politician and actors from other sectors, resulted in the elaboration of an innovative educational plan, composed according to the principles of ESD. At the same time, future strategies should focus on integrating actors from other important areas such as schools. In addition, strategies that foster trans-regional cooperation would be helpful with the diffusion of ESD.

With respect to some of the municipalities, a future strategy that fosters greater participation of stakeholders from other areas of activity, as required by the UN’s International Implementation Scheme (IIS) and the National Action Plan of the UN Decade may be helpful in promoting the implementation and diffusion of the social innovation ESD. Business actors and teachers, in particular, complain about not being sufficiently integrated into ESD networks and that the same people always take control and create turf wars. Furthermore, a lack of transparency and information exchange on existing ESD projects was seen. Business actors in these municipalities faced biases from other actors concerned that they ignored ecological and social dimensions of sustainable development. In some municipalities, ESD is mainly concentrated on environmental topics and many ESD actors express reservations about business aims. However, if different sectors are not integrated, it’s difficult to achieve a balance between ecological, economic, and social dimensions, as it has been proclaimed by the concept of sustainable development as such.

This article has explored the role of Social Network Analysis in analyzing and supporting innovation processes. In order to better understand the opportunities of SNA in innovation research, the author presented empirical results of her own quantitative and qualitative research on innovation networks in five German municipalities actively engaged in the field of ESD. The article showed the value of using a combination of both quantitative and qualitative SNA in order to better understand how and why social innovations are implemented and the opportunities to further develop the network.

Quantitative SNA was implemented to analyze the impact of structural characteristics of social networks on the implementation and the diffusion of the social innovation of ESD. It was discovered, for example, that cooperation in the field of ESD mainly takes place within municipalities and that cooperation beyond municipal borders is low and marked by structural holes. Furthermore, it was shown that social networks in the area of ESD are mostly composed of small and dense groups each representing different sets of actors (e.g., local administration, educational institutions, and business) and pursuing different interests and ideas under the umbrella of ESD. Weak ties, on the contrary, are very important in the field of ESD as they are responsible for the diffusion of innovations.

However, structural holes also exist within the municipalities with respect to the quality of the relations. The extreme example of Erfurt illustrated how the development of new ideas can be hampered by structural weaknesses. In contrast, cooperation and innovation development in the field of ESD are regarded to work well in Gelsenkirchen. In Alheim, actors from different sectors are integrated. Most central roles are played by non-formal education actors, whereas one central role is wielded by a politician. In terms of innovation diffusion, Alheim can be regarded as a best practice. Not least, cooperation beyond institutional borders works well and individual clustering values are low: persons are not always connected to the same clusters. Finally, the implementation of ESD in Alheim benefits from strong relations between one well-connected political and actors from other sectors. The central politician connects different areas of activity and promotes the integration of ecological, economic, and social dimensions in terms of sustainable development.

Quantitative techniques of SNA enabled to identify innovation networks, to determine network boundaries, to define actors within the innovation network, and to investigate the network position of actors. Problems of coordination, information, and qualitative relations were discussed. At the same time, quantitative SNA was shown unable to analyze reasons, motivations, and perceptions behind network structure. These issues were then analyzed by using qualitative SNA methods, such as network maps. A combination of qualitative and quantitative SNA techniques may thus prove the most fruitful for innovation research. In order to better understand the role of social networks in the diffusion of social innovations and to generate knowledge related to innovation potential and courses of action, qualitative techniques were used to supplement the quantitative analysis. It was assumed that the costs of information exchange are not only material (money, time), but also social. Conflicts and lack of confidence between actors, for example, may prevent successful innovation diffusion. Qualitative egocentric network maps could validate quantitative results as well as disclose subjective perceptions and orientations. The central position of one politician in Alheim could thus be traced back to its discursive and structural power. Actors in Alheim have great trust in the ideological competences of the well-connected person who supports the establishment of ESD in many sectors. Visualizations with qualitative network maps support the completion of the interview situation with visual representations. Visualized networks can also serve as mental or cognitive assistance. In combination with quantitative results, however, qualitative network maps enable us to detect where and how innovations and development processes may be possible due to structural and subjective conditions. Finally, compared to conventional statistical analysis that treat structural and individual levels as separately, analyses and visualizations of network data give us more information about the influence of social relations. SNA enables us to capture the interaction between actors and social context, to better understand how innovations are implemented and diffused, to analyze how and why social or educational change takes place or does not take place, and to disclose opportunities for future strategies.

This contribution has shown that SNA can begin to answer questions related to innovation processes. I hope it will open new avenues for further uses of SNA in innovation and futures research.

At the same time, there is little research on the democratic implications of network governance [ 5 ] as well as on the strengths and limits of the concept related to issues of educational innovations such as Education for Sustainable Development (ESD).

When examined through the framework of Social Network Analysis (SNA), the deficits of the concept of ‘Educational Governance’ become evident. In the scientific literature and in educational and political praxis, the concept of Educational Governance is often exclusively related to institutions of formal learning such as schools or educational training. In this manner, it is not possible to capture the real boundaries of social networks and to conceptualize social networks as can be done with SNA techniques. Furthermore, many actors, initiatives, and activities that play an important role in learning processes are analytically excluded in current applications of Educational Governance. For that reason, this article does not use an Educational Governance approach. Instead, it uses a governance approach that draws on theoretical concepts developed in social science.

Qualitative network maps were gathered in cooperation with a research project coordinated by Inka Bormann.

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I thank the editors and two anonymous reviewers for their constructive comments, which helped me to improve the article. The article is based on results of a study I conducted at the Freie Universität Berlin. I would also like to thank Gerhard de Haan for useful information and for supporting my research.

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Kolleck, N. Social network analysis in innovation research: using a mixed methods approach to analyze social innovations. Eur J Futures Res 1 , 25 (2013). https://doi.org/10.1007/s40309-013-0025-2

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Social network analysis in business and management research: A bibliometric analysis of the research trend and performance from 2001 to 2020

Adhe rizky anugerah.

a Bioresource Management Lab, Institute of Tropical Forestry and Forest Products (INTROP), Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia

Prafajar Suksessanno Muttaqin

b Department of Logistics Engineering, School of Industrial and System Engineering, Telkom University, 40257 Bandung, Indonesia

Wahyu Trinarningsih

c Faculty of Economics and Business, Universitas Sebelas Maret, 57126 Surakarta, Indonesia

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Data included in article/supplementary material/referenced in article.

In the past years, research in Social Network Analysis (SNA) has increased. Initially, the research area was limited to sociology and anthropology but has now been used in numerous disciplines. The business and management discipline has many potentials in employing the SNA approach due to enormous relational data, ranging from employees, stakeholders to organisations. The study aims to analyse the research trend, performance, and the utilisation of the SNA approach in business and management research. Bibliometric analysis was conducted by employing 2,158 research data from the Scopus database published from 2001 to 2020. Next, the research quantity and quality were calculated using Harzing's Publish or Perish while VOSviewer visualised research topics and cluster analysis. The study found an upward trend pattern in SNA research since 2005 and reached the peak in 2020. Generally, six subjects under the business and management discipline have used SNA as a methodology tool, including risk management, project management, supply chain management (SCM), tourism, technology and innovation management, and knowledge management. To the best of the authors' knowledge, the study is the first to examine the performance and analysis of SNA in the overall business and management disciplines. The findings provide insight to researchers, academicians, consultants, and other stakeholders on the practical use of SNA in business and management research.

Social network analysis; Bibliometrics; Clustering analysis; Business and management; Literature.

1. Introduction

The SNA is a theory investigating the relations and interactions based on anthropology, sociology, and social psychology to assess social structures ( Erçetin and Neyişci, 2014 ). The social structure in a network theory comprises individuals or organisations named nodes linked through one or more types of interdependencies, such as friendship, kinship, financial exchange, knowledge or prestige ( Parell, 2012 ). The actors range across different levels, from individuals, web pages, families, large organisations, and nations. Nowadays, SNA usage has grown, utilised in anthropology and sociology and several fields of science, including business and management disciplines.

However, studies on the SNA trends and applications in business and management are limited. Although published articles provide a catalogue of SNA concepts, they lack explanatory mechanisms on its application ( Borgatti and Li, 2009 ). Thus, the study aims to assess publication performances and explore SNA usage in business and management studies using Bibliometric analysis. The bibliometric methodology has been widely used to provide quantitative analysis of written publications using statistical tools ( Ellegaard and Wallin, 2015 ). It can help detect established and emergent topical areas, research clusters and scholars, and others ( Fahimnia et al., 2015 ). This analysis reveals important publications and objectively depicts the linkages between and among articles about a specific research topic or field by examining how frequently they have been co-cited by other published articles ( Fetscherin and Usunier, 2012 ).

Bibliometric analysis has at least two primary objectives: 1) to quantitatively measure the quality of journals or authors using statistical indicators such as citations rates ( Vieira et al., 2021 ), and 2) to analyse the knowledge structure and development of specific research fields ( Jing et al., 2015 ). Hence, the study addresses the following research questions: RQ1: What is the current research trend of SNA in business and management research ? RQ2: What is the most productive year of SNA in the business and management discipline ? RQ3: What are the most influential and productive institutions, authors, journals, and countries ? RQ4: What is the use of SNA in the business and management discipline and their cluster topics in the past 20 years ?

Several literature reviews and bibliometric papers on the use of SNA in general business and management areas have been published, but the number is limited. Monaghan, Lavelle, & Gunnigle (2017) analysed SNA usage in management research and practice to discuss the critical dimensions for handling and analysing network data for business research. The authors discovered four dimensions in initial engagement with SNA in business and management research: structure of research design, data collection, handling of data and data interpretation. Nonetheless, studies did not explain the distribution of research clusters and how SNA can be used in practical business and management research. Specifically, Su et al. (2019) conducted a Bibliometric analysis on SNA literature with no limitation of subject discipline and collected the data from Web of Science (WoS), covering 20 publication years from 1999 to 2018. Nevertheless, Su et al. (2019) mainly discussed the SNA publication performance but not how the approach was used previously.

In the more specific subject area of business and management, SNA has been explored to unveil the relationship between organisations, as conducted by Sozen et al. (2009) . The SNA has been used to measure the organisations' social capital, map resource dependency relations, and discover coalitions and cliques between organisations. Kurt and Kurt (2020) have explored the potential of SNA in international business (IB) research, because of two fundamental phemomena: firm internationalisation and multinational enterprises (MNEs). From the marketing perspective, SNA could detect the most influential actors to efficiently spread a message in online communities for marketing purposes ( Litterio et al., 2017 ).

The current study conducted a clustering analysis to identify and analyse SNA performance and its application in general business and management discipline using bibliometrics information. Thus, academicians, managers, consultants, and other stakeholders could understand when and how to apply the SNA approach. For instance, SNA can identify potential risks contributing to schedule delays in project risk management ( Li et al., 2016 ). The discussion section explores how SNA has been previously used in business and management research. Besides, the study addresses the problem in Borgatti and Li (2009) , exploring the actual application of SNA in management and business research.

2.1. Data sources and search strategy

The primary study objective is to analyse the research trend and explore the SNA approach in business and management research. A Bibliometric analysis was employed due to its accuracy in quantifying and evaluating scientific publications ( Carmona-Serrano et al., 2020 ). Additionally, the data were collected through the Scopus database. Although Scopus and WoS are the main and most comprehensive sources for Bibliometric analysis, Scopus has more advantages: more inclusive content coverage, more openness to society, and available individual profiles for all authors, institutions, and serial sources. Additionally, many papers have confirmed that Scopus provides wider overall coverage and Scopus indexing a greater amount of unique sources not covered by WoS ( Pranckutė, 2021 ). In the business, economics, and management area, 89% of articles listed in WoS are listed in Scopus. Hence, the study area (business and management) chose the Scopus database for further analysis.

The next step involved determining the search string, including all documents with the title, abstract, and keywords containing "network analysis" or "Social Network Analysis". These two main keywords are representative enough to reach the objective; they are not too wide and specific. The main goal is the utilisation of SNA as a concept and as a methodology can be widely captured. These two versions of keywords “Social Network Analysis” and “Network Analysis” without the “social” has a significant impact. Some articles did not put the complete sentence of SNA, although the articles mainly discussed the concept of network analysis. One of the examples is the ownership structure related research developed by Vitali et al. (2011) which changed the word "social network analysis" to "corporate network analysis". The term “social” in SNA refers to people interaction, while in the operation research, the relationship could be between airport, stakeholders, corporation, etc.

In the first run, 113,945 research related to SNA was found in the Scopus database, mainly Engineering and Computer Science fields. Besides, the search results were limited to the subject area in business, management, and accounting and covered publication from 2001 to 2020 (20 years). The study also excluded non-journal articles, such as conference proceedings, trade reports, book chapters, and others.

The search limitations have resulted in 2,881 articles, but many were still not related to network analysis or business and management. Further, 723 articles were excluded, covering articles in neuroscience, bibliometric, circuit network (engineering), earth and planetary science, chemistry, etc., although the articles employed network analysis as a methodology. The exclusion was also applied to articles that use SNA in multi-subject journals with little or no explanation in business, management, and accounting perspectives. One example is the Journal of Cleaner Production listed in four subject areas: business, management and accounting; energy; industrial and manufacturing engineering; and environmental science. In this journal, SNA theory is used to identify the relationship between ecosystems by measuring the flow of energy or material between organisms, which has little or no explanation from business and management perspectives. At the end of the search, 2,158 articles were extracted for further analysis. The flow chart on the data collection strategy is presented in Figure 1 .

The search strategy flow diagram (adopted from ( Zakaria et al., 2021 )).

2.2. Data analysis

The first stage involved analysing the data descriptively to identify the quality and quantity using standard Bibliometric measures ( Hirsch, 2005 ). The total number of publications (TP) assessed the quantity dimension, whereas other metrics assessed the quality dimension, such as total citation (TC), number of cited publications (NCP), average citation per publication (C/P), average citations per cited publication (C/CP) ( Hirsch, 2007 ). Additionally, the g-index ( g ) and h-index ( h ) are usually included in the Bibliometric measures to predict future achievement rather than standard measures. The indicators are applied to various levels: country-level, organisation-level, journal-level, and author-level. The information is processed and analysed using Harzing publish or perish (PoP) software by extracting Research Information System (RIS) data from the Scopus database.

The second stage visualised the research network to understand the relationship between nodes, including authors, affiliations (organisations), countries, citations, and keywords. Nonetheless, only keywords co-occurrence was carried out to examine the past, current, and future potential of SNA in business and management research. The study analysed the keywords based on the frequency, edges, and clusters. The combination between nodes (keywords) and edges (the relationship between keywords) form clusters with numerous research themes ( Dhamija and Bag, 2020 ). The bigger nodes show a higher occurrence in the keyword visualisations, and the thicker edges show the higher link strength. Meanwhile, cluster analysis in the study represents a set of similar keywords in one group, different in other groups to identify the research interest and keywords combination within the group. The cluster mapping was performed by VOSviewer, an open-access programme to construct and view Bibliometric maps ( van Eck and Waltman, 2010 ). Besides, the study developed an overlay visualisation to explore research evolution in SNA over time.

3.1. Description of retrieved literature

The study is limited to SNA research in business and management research published between 2001 to 2020. The study also excluded review papers, conference papers, editorials, and other documents besides journal articles for further analysis. Ultimately, the study retrieved a total of 2,158 articles. Although the search was limited to only English articles, the study identified seven bilingual articles in Spanish (3 articles), Chinese (2 articles), Lithuanian (1 article), and Portuguese (1 article). The articles had 58,522 citations, an average of 2,926 citations per year, and 27 citations per paper. The complete citation metrics for the articles are shown in Table 1 .

Table 1

Citations metrics.

Besides SNA as a primary keyword, the top keywords were "innovation" (6.16%), "project management" (3.48%), "knowledge management" (3.29%), "decision making" (2.97%), "complex networks" (2.69%), and others. The top keywords are listed in Table 2 . High-frequency keywords show the popularity of a specific topic ( Pesta et al., 2018 ). The listed keywords in the study usually appear together in SNA and are used to explore the potential use of SNA in business and management research.

Table 2

Top keywords.

3.2. Research Growth

Although SNA research productivity presented the ups and downs throughout the year, a consistent upward trend was found in the pattern. Research in the first five years (2001–2005) was limited and never reached 30 publications per year. In 2002, only 10 articles were published, increasing almost five times in 2007 (n = 49 documents). The number increased until 2020, slightly decreasing in 2011, 2015, and 2019. Meanwhile, the most productive year was in 2020, with 334 published articles.

The articles published in 2001 had the highest average citation per publication (c/p = 186.69). However, the highest h-index were in 2010 ( h = 43) and 2014 ( h = 36) which indicate high cumulative impact of the articles measured by its quantity with quality. Low citations per publication in recent years were expected due to increasing citation counts over time. The publication trend and average citations per publication are presented in Figure 2 .

Total publications and citations by year.

3.3. Top countries, institutions, and authors in SNA business and management research

The United States (US), the United Kingdom (UK), and China were the most prolific countries with 605, 230 and 215 articles, respectively. The study discovered no dominating continent that produced SNA business and management research, and all were equally distributed except for Africa. The top ten countries list (see Table 3 ) showed one North American, four Asian and Oceanian, and five European countries. As the top most productive countries, the United States and the United Kingdom published quality articles with highest average citation per publication of 39.34 and 32.80, respectively. Meanwhile, Asian countries had small citations (based on the top ten most productive countries): South Korea (c/p = 21.84) and China (c/p = 27.08) were at the bottom of the ranking according to the c/p calculation.

Table 3

The top ten countries contributed to the publications.

Notes: TP = total number of publications; NCP = number of cited publications; TC = total citations; C/P = average citations per publication; C/CP = average citations per cited publication; h = h-index; and g = g-index.

Hong Kong Polytechnic University was the most productive institution with 45 published articles and had the highest h- and g- index (see Table 4 ). The publication number was higher than the second most productive, Università Bocconi (n = 25). However, the top ten list showed that the University of Arizona had the highest c/p with 102.47, followed by the University of Kentucky (c/p = 58.40) and the Università Bocconi (c/p = 58.36). As the most productive country, there were four United States universities listed as the top most productive institutions but it only covered 10.9% from the total US articles. This indicates that the publications were distributed to other US institutions. Nevertheless, articles from two Hong Kong insitutions, Hong Kong Polytechic University and City University Hongkong accumulated 57 articles (82.61% of the country's total articles).

Table 4

Top 10 most influential institutions in SNA (business and management) research.

The two most productive institutions conducted different research themes. Figure 3 shows that Hong Kong Polytechnic University use SNA in numerous areas, such as "supply chain management", "transportation", "big data", "knowledge management", “stakeholder analysis in construction project” and others. Nonetheless, Università Bocconi research mostly involved tourism; the keywords were "tourist destination", "tourism management", "stakeholder", and “hospitality”. Besides, 17 of the 25 articles (68.0%) from Università Bocconi were written by Rodolfo Baggio, the author with the most article in SNA business and management. Table 5 presents the most productive authors with at least six published articles in SNA.

Research topic comparison between A) Hong Kong Polytechnic University and B) Università Bocconi.

Table 5

Most productive authors with a minimum of six articles.

Rodolfo Baggio was the most productive author with the most publications, followed by Noel Scott (n = 8) from Australia and Andrea Fronzetti Colladon (n = 7) from Italy. Based on the average citations per document, Carlos Casanueva from the Universidad de Sevilla, Spain, had the highest score with an average of 95.33 citations per document. Baggio, R. and Scott, N, as the most productive authors have similar research interest, which is in tourism management. Based on the study database, they had written four articles together using a network analysis approach in tourism management. Fronzetti Colladon, A. is an expert in big data, creativity and innovation management while Hossain L., utilising SNA in organisational communication network during crisis or emergency events.

3.4. Most active journals

The majority of the articles were mostly published in Elsevier's journals. Specifically, seven out of 12 source titles were Elsevier's journals; two journals were published by the American Society of Civil Engineers (ASCE), two journals by Taylor's and Francis, and one by Emerald. The Journal of Technological Forecasting and Social Change was the most active source with 84 articles, followed by Transportation Research Part E: Logistics and Transportation Review and Knowledge based Systems with 45 and 44 articles, respectively. Based on the average citations per publication, Construction Management and Economics had the highest score (c/p = 78.50), followed by Decision Support Systems (c/p = 55.33) and Transportation Research Part E (c/p = 50.51). Table 6 demonstrates a list of the most active sources in publishing research in SNA (business and management) and its impact score (cite score and SCImago Journal Rank (SJR) 2019).

Table 6

Most active source title.

Notes: TP = total number of publications; C/P = average citations per publication.

The SNA usage in business and management varies and depends on the journal scope. Particularly, SNA is used to study the interaction between people in the social environment and in various other subjects. The use of SNA in specific journals was explored by visualising the network of keywords relationship, as presented in Figure 4 . The selected three journals publishing research in SNA (business and management) had a different perspective in employing SNA as a tool to analyse the relationship between nodes.

Most frequent keywords in a) Technological Forecasting and Social Change; b) Transportation Research Part E: Logistics and Transportation Review; and c) Journal of Business Research.

Journal of Technological Forecasting and Social Change primarily utilised "innovation", "technological development", "patents and inventions", "technology adoption", "emerging technology", and others. SNA can also be used for transportation research as published in Transportation Research Part E: Logistics and Transportation Review with keywords "numerical model", "transportation planning", "air transportation", "optimisation", "freight transport", and others. Meanwhile, research published in Journal of Business Research published articles with keywords “business networks”, “social closure”, “collaboration”, “diffusion”, and others. The SNA can also be used in construction and project management, tourism management, urban planning and development, and organisational study (coordination and competition).

3.5. Highly-cited articles

Tsai (2002) published the top-cited article in SNA business and management research titled " Social structure of "coopetition" within a multiunit organisation: Coordination, Competition, and Intra organisational Knowledge Sharing" . The publication had 1,124 or 56.2 citations per year. Besides, the article explored another use of SNA, explained in the "most active journals" section in the organisational study. The study revealed that most of the top 15 articles related to inter- or intra- organisational networks, and some papers explored the use of SNA in social communication, supply chain, tourism marketing, and others. Table 7 shows the top 15 highly-cited articles.

Table 7

Top 15 Highly-cited articles.

3.6. The use of SNA in business and management research

The study conducted a keywords cluster analysis to highlight SNA usage in business and management research and identify how keywords are linked. The keywords cluster analysis was presented in two ways; the first is based on the occurrence level (see Figure 5 ), and the second is based on the year of publications (see Figure 6 ). Based on the level of keywords occurrence, SNA research was classified into six clusters: cluster 1 (red nodes) covered research in construction, project management, and information management; cluster 2 (green nodes) covered research in transportation and tourism management; cluster 3 (dark blue nodes) covered research in semantic, big data, and decision support system; cluster 4 (yellow nodes) included research in innovation, international trade, and globalisation; cluster 5 (purple node) explored research in knowledge management and knowledge sharing; cluster 6 (light blue nodes) included research in social capital, and financial performance and management.

Keywords analysis of SNA in business and management publications.

Keywords evolution of SNA in business and management research.

According to publication years, the study discovered that from 2012 to 2014, the most frequent keywords were "project management", "optimisation", "technology transfer", and "construction industry". From 2015 to 2016, the keywords shifted to "data mining", "information management", "decision-making", "tourist destination", "air transportation", "airline industry", and "innovation". Recently, "sentiment analysis", "text mining", and "big data" became popular in SNA research.

4. Discussion

The study was conducted to analyse SNA research in business and management subjects. Generally, an upward trend was found in the number of publications, significantly increasing since 2005. A significant increase was also discovered in 2020, a 40.3% increase from the previous year, the second-highest increase in the past 20 years. A similar Bibliometric analysis on SNA research without subject limitation had a similar pattern with the study, whereby SNA publications increased gradually since 2005 Based on quality metrics, articles published in 2001 and 2002 had the highest average citations per publication. Moreover, several articles published in those years also had the highest number of citations published in organisational science.

Tsai (2002) articles had the highest citation number (c = 1,124), followed by Reagans and Zuckerman (2001) , with 1009 citations. Tsai (2002) displayed intra organisational as a set of social networks and examined networks of collaborative and competitive ties within the organisation. Each unit collaborates for knowledge sharing and competes for resources and market share. Additionally, the centrality concept was used to measure the ability of intra organisational units in the knowledge sharing behaviour. In the same journal, Reagans and Zuckerman (2001) employed the SNA approach to examine the relationship between team density and heterogeneity to its performance using two network metrics: network density to assess communication frequency between team members, and network heterogeneity to explore time allocation of scientists to colleagues far removed in the team tenure distribution. According to above explanation, SNA can be used at different organisation levels, one in unit levels-organisation, and another in person-level interactions under one team.

The study revealed the US, the UK, and China as the most productive countries. Moreover, the study had similar findings as in Su et al. (2019) ; they found that the US had dominated the research in SNA, UK ranked second, and followed by China. Based on the average c/p, institutions from Asia such as South Korea, China, and Taiwan (average c/p = 23.77) received lower scores than European institutions (average c/p = 28.31). Better quality and impactful research are needed for authors from Asian institutions. The following section describes SNA usage in business and management themes.

4.1. Research themes

The SNA is a set of formal methods for studying social structures according to graph theory. Individuals and social actors, such as groups and organisations, are shown in points and their social relations in lines ( Korom, 2015 ). Meanwhile, the structure relations and the location of individual actors have substantial behavioural consequences for individuals and social structure as a whole ( Wellman, 1988 ). The SNA has become a multidisciplinary endeavour extending beyond sociology and social anthropology sciences and to many other disciplines, such as politics, epidemiology, communication science, and others. The next section explores the use of SNA in business and management discipline from publication records between 2001 to 2020 by analysing the keywords co-occurrences.

4.2. Project management

The first paper that employed SNA in project management was published in 1997 by Loosemore, believing that construction project participants are embedded in complex social networks that are constantly changing. Furthermore, Loosemore (1997) analysed communication efficiency in the engineering project organisation during crises. Besides, Pryke (2004) had the highest citations in the construction and project management area. Contrary to Loosemore, who applied network analysis at the individual level, Pryke examined the relationship between project actors at the firm level. Pryke also used network analysis in the comparative analysis of procurement and project management of construction projects. Subsequently, SNA publication in project management literature increased substantially.

Chinowsky, Diekmann, & O'Brien (2010) summarised the use of network analysis in project management research. After examining communication efficiency, network analysis analysed networks in relationship-based procurement, the effect of centrality on project coordination, the effect of cultural diversity on project performance, collaboration effectiveness to achieve high-performance teams, and others. The study discovered several popular keywords in project management research, such as "communication", "decision-making", "stakeholder", "accident prevention", "scheduling", "information exchange", "collaborative projects" and others, which explained the use of SNA in this subject. The top three journals publishing SNA usage in project and construction management were the Journal of Construction Engineering and Management (ASCE), International Journal of Project Management (Elsevier), and Construction Management and Economics (Taylor & Francis).

4.3. Risk management

The SNA publications in risk assessment and management usually relate to other subjects, such as project management, SCM, knowledge management, and others. The study found 42 related articles, mostly on construction and project management. Notably, SNA improved the effectiveness and accuracy of stakeholder and risk analysis in green building projects ( Yang et al., 2016 ). The model considered the risk associated with stakeholders and the interdependencies of risks for better decision-making. Additionally, Li et al. (2016) employed SNA for risk evaluation and risk response processes in construction projects.

The SNA effectively evaluates the potential risk contributing to schedule delays in project processes by removing key nodes and links, ultimately removing stakeholder risk that is highly interconnected to another risk. Similar to Li, Yu et al. (2017) specifically utilised SNA for social risk in urban redevelopment projects during the housing demolition stage with an identical process. The approach extends beyond construction and project management subject and any other subjects that employed risk assessment or evaluation in their risk management process.

4.4. tourism management

The SNA approach in tourism-related subjects was widespread, with "tourist destinations" and “tourism” among the most frequent and top 25 keywords (see Table 2 ). Besides, Baggio, R. was the most productive author who used SNA in tourism research. Three research streams are related to network analysis in the travel industry setting: the Bibliometric analysis on research collaboration and knowledge creation; network analysis on the travel industry supply, destination, and policy systems; and tourist movements and behavioural patterns ( Liu et al., 2017 ). Besides, the study discovered three significant journals with the most publications in tourism research: Annals of Tourism Research, Tourism Management, and Current Issues in Tourism, similar to Casanueva et al. (2016) ; whereby tourism management was the most productive journal. Recently, the Annals of Tourism Research surpassed Tourism Management.

Most tourism supply and destination research highlighted collaboration and partnership among tourism stakeholders. Baggio, Scott and Cooper (2010) applied network analysis to explain the topology of stakeholders in Elba, Italy's tourism organisations (hotels, travel agencies, associations, public bodies, and others). The tourism stakeholders were described in quantitative (network metrics) and qualitative (figure) ways. For instance, the percentage of non-connected networks described the sparseness of a network and showed a low degree of collaboration or cooperation between stakeholders. Nonetheless, Leung et al. (2012) and Asero et al. (2016) , and others studied tourist movement and behavioural patterns by analysing tourists' itineraries with traditional (interview or online travel diaries) or more-advanced technologies (geographic information system (GIS), global positioning system (GPS), timing systems, camera-based systems, and others). The primary objective is to analyse the main tourist attraction, main tourism movement patterns and change patterns in tourist attractions.

4.5. Supply chain management

The use of SNA in supply chain management research had developed in 2010, and numerous scholars were unaware of the possibilities of the SNA approach in the SCM field ( Wichmann and Kaufmann, 2016 ). The supply chain is a network of companies comprising interconnected actors, such as suppliers, manufacturers, logistic providers, and customers ( Bellamy et al., 2014 ). Three journals with the most SNA articles in SCM are the International Journal of Production Economics, International Journal of Production Research, and Journal of Operations Management.

Y. Kim et al. (2011) employed SNA to analyse the structural characteristics of supply networks in a buyers-suppliers network of automotive industries. The networks in the supply chain were classified into the material flow (supply load, demand load, and operational criticality) and the contractual relationship between actors (influential scope, informational independence, and relational mediation). The study discovered that network metrics could be used to analyse the characteristic of supply network structures.

The SNA can also measure and reduce supply chain complexity ( Allesina et al., 2010 ), a concept based on ecological theory. Additionally, eight entropic performance indexes were used: total system throughput, average mutual information, development capacity, overhead in input, export, and dissipation, etc. Contrarily, Ting & Tsang (2014) utilised SNA to identify the possibility of counterfeit products from infiltrating into the supply chain using the transaction records history to detect problematic parties and their suspicious trails. Three SNA measures were included in the study: degree centrality, betweenness centrality, and closeness centrality.

4.6. Knowledge management

The SNA usage in knowledge management varies; for instance, Parise (2007) used SNA for knowledge management of human resources, including knowledge creation and innovation, knowledge transfer and retention, and job succession planning. The SNA in knowledge creation and innovation is used to identify the flow of ideas and bottlenecks in the decision-making process. The SNA is also applied to analyse the structure of regional knowledge in the technology specialisation ( Cantner et al., 2010 ), knowledge transfer analysis on sustainable construction projects ( Schröpfer et al., 2017 ), predicting and evaluating future knowledge flows in insurance organisations ( Leon et al., 2017 ) and knowledge transfer from experts to newcomers ( Guechtouli et al., 2013 ), and others. The top productive journals are the Journal of Knowledge Management, Technological Forecasting and Social Change, and the Journal of Construction Engineering and Management.

4.7. Technology and innovation management

The SNA is used for innovation and technology transfer. Keywords under this subject include "citation "innovation", "patents and inventions", "technological development", and others. The SNA metrics utilised to assess the performance and centrality of individuals in virtual research and design (R&D) groups by analysing their e-mails ( Ahuja et al., 2003 ), identifying the position and relationships between innovators ( Cantner and Graf, 2006 ), research collaboration network between university-industry ( Balconi and Laboranti, 2006 ), and others.

The SNA in patents and inventions identify companies with a significant legal influence on the applied technologies by analysing intellectual property lawsuits between companies (H. Kim and Song, 2013 ). Patents data were also popular to study technological innovation of electronic companies; SNA was employed to cluster the patents and find vacant technology domains ( Jun and Sung Park, 2013 ). Furthermore, patent data in SNA enables exploring the technology evolution of certain products ( Lee et al., 2010 ). Specifically, the top three journals were: Technological Forecasting and Social Change, Technological Analysis and Strategic Management, and Industry and Innovation.

5. Conclusion

After SNA was introduced in 1969 by Mitchell, many researchers from various fields were interested in studying the relationship between nodes. The most frequent disciplines that used SNA are sociology, anthropology, social psychology, and communication. Nevertheless, SNA usage in business and management discipline was limited. Hence, the study analysed the trend and performance of SNA in the business and management discipline from 2001 to 2020. The study revealed a steady upward trend of publications in this field and increased significantly since 2005. The US, the UK, and China were the most productive countries. Although the study found three Asian institutions as the most productive countries, the average c/p was lower than the European and American countries. Besides, SNA as a research tool has been published in multidisciplinary journals, ranging from Journal of Management in Engineering to Journal of Knowledge Management, depending on the subject of investigation.

The study also performed a co-occurrence keywords analysis to examine the research cluster and emerging research topics in SNA, especially in business and management studies. The study revealed six clusters, each containing one to two research disciplines. The SNA has been employed in numerous topics, including project management, risk management, tourism management, supply chain, knowledge management, and technological management. Observably, big data, social media and sentiment analysis are the trending topic in SNA.

The research contributions include: first, the publication trend and research productivity show the current issue and development of SNA in the business and management discipline; second, the data on most productive authors and institutions academic communication and cooperation among scholars in related fields; and lastly, the visualisation of research topics mapping and the cluster analysis explored the current use of SNA in different discipline and formulated future research agenda.

The study limitations are: first, the study only considered the Scopus database and SNA literature could be more extensive. Other significant databases, such as WoS and CNKI (Chinese National Knowledge Infrastructure) should be considered for future research. Second, in the co-occurrence keywords analysis, a threshold was set to limit important keywords; thus, the study might not include several research topics using SNA. Third, although the study has cleaned the database, titles not purely from business and management disciplines might be included due to journal sources with multi subject classification. Lastly, we only employed standard bibliometric measures as a quantitative assessment in this research; the inclusion of SNA’ centrality index can be considered in future study to assess the power and importance of authors, institutions, countries, and journals.

Institutional review board statement

Not applicable.

Informed consent statement

Decalarations, author contribution statement.

All authors listed have significantly contributed to the development and the writing of this article.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

Declaration of interests statement.

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

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Social network analysis is the process of investigating social structures through the use of networks and graph theory. It combines a variety of techniques for analyzing the structure of social networks as well as theories that aim at explaining the underlying dynamics and patterns observed in these structures. It is an inherently interdisciplinary field which originally emerged from the fields of social psychology, statistics and graph theory. This talk will covers the theory of social network analysis, with a short introduction to graph theory and information spread. Then we will deep dive into Python code with NetworkX to get a better understanding of the network components, followed-up by constructing and implying social networks from real Pandas and textual datasets. Finally we will go over code examples of practical use-cases such as visualization with matplotlib, social-centrality analysis and influence maximization for information spread.

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Many Americans use social media to connect with one another, engage with news content, share information and entertain themselves. Explore the patterns and trends shaping the social media landscape.

To better understand Americans’ social media use, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023. Ipsos conducted this National Public Opinion Reference Survey (NPORS) for the Center using address-based sampling and a multimode protocol that included both web and mail. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, education and other categories.

Polls from 2000 to 2021 were conducted via phone. For more on this mode shift, read our Q&A.

Here are the questions used for this analysis , along with responses, and  its methodology ­­­.

A note on terminology: Our May-September 2023 survey was already in the field when Twitter changed its name to “X.” The terms  Twitter  and  X  are both used in this report to refer to the same platform.

YouTube and Facebook are the most-widely used online platforms. About half of U.S. adults say they use Instagram, and smaller shares use sites or apps such as TikTok, LinkedIn, Twitter (X) and BeReal.

Note: The vertical line indicates a change in mode. Polls from 2012-2021 were conducted via phone. In 2023, the poll was conducted via web and mail. For more details on this shift, please read our Q&A . Refer to the topline for more information on how question wording varied over the years. Pre-2018 data is not available for YouTube, Snapchat or WhatsApp; pre-2019 data is not available for Reddit; pre-2021 data is not available for TikTok; pre-2023 data is not available for BeReal. Respondents who did not give an answer are not shown.

Source: Surveys of U.S. adults conducted 2012-2023.

Usage of the major online platforms varies by factors such as age, gender and level of formal education.

% of U.S. adults who say they ever use __ by …

• RACE & ETHNICITY
• POLITICAL AFFILIATION

This fact sheet was compiled by Research Assistant  Olivia Sidoti , with help from Research Analyst  Risa Gelles-Watnick , Research Analyst  Michelle Faverio , Digital Producer  Sara Atske , Associate Information Graphics Designer Kaitlyn Radde and Temporary Researcher  Eugenie Park .

Follow these links for more in-depth analysis of the impact of social media on American life.

• Americans’ Social Media Use  Jan. 31, 2024
• Americans’ Use of Mobile Technology and Home Broadband  Jan. 31 2024
• Q&A: How and why we’re changing the way we study tech adoption  Jan. 31, 2024

Find more reports and blog posts related to  internet and technology .

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ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

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Social Media & User-Generated Content

• Global social networks ranked by number of users 2024

Market leader Facebook was the first social network to surpass one billion registered accounts and currently sits at more than three billion monthly active users. Meta Platforms owns four of the biggest social media platforms, all with more than one billion monthly active users each: Facebook (core platform), WhatsApp, Facebook Messenger, and Instagram. In the third quarter of 2023, Facebook reported around four billion monthly  core Family product users .

The United States and China account for the most high-profile social platforms

Most top ranked social networks with more than 100 million users originated in the United States, but services like Chinese social networks WeChat, QQ or video sharing app Douyin have also garnered mainstream appeal in their respective regions due to local context and content. Douyin’s popularity has led to the platform releasing an international version of its network: a little app called TikTok .

How many people use social media?

The leading social networks are usually available in multiple languages and enable users to connect with friends or people across geographical, political, or economic borders. In 2022, Social networking sites are estimated to reach 3.96 billion users and these figures are still expected to grow as mobile device usage and mobile social networks increasingly gain traction in previously underserved markets.

Most popular social networks worldwide as of April 2024, ranked by number of monthly active users (in millions)

Additional Information

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DataReportal

social networks and messenger/chat app/voip included; figures for TikTok does not include Douyin

¹Platforms have not published updated user figures in the past 12 months, figures may be out of date and less reliable

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