Social Network Analysis Research Research Articles

Bibliometric and visualized analysis of social network analysis research on Scopus databases and VOSviewer

Bibliometric and visualized analysis of social network analysis research on Scopus databases and VOSviewer

Social Network Analysis: A Survey on Process, Tools, and Application

Due to the explosive rise of online social networks, social network analysis (SNA) has emerged as a significant academic field in recent years. Understanding and examining social relationships in networks through network analysis opens up numerous research avenues in sociology, literature, media, biology, computer science, sports, and more. Therefore, certain studies review and discuss some research verticals of SNA, such as viral marketing, information diffusion, clustering, link prediction, and so on, to provide background knowledge and understanding. These studies still lack the SNA process, tools, and practical aspects in multidisciplinary applications. Inspired by these facts, we have discussed the background, process, tools, and application of SNA. First, we have presented a detailed description of the SNA process. Thereafter, we presented a comparative analysis of SNA tools and languages. Finally, we have discussed the various applications corresponding to SNA research verticals.

The effect of distant connections on node anonymity in complex networks

Ensuring privacy of individuals is of paramount importance to social network analysis research. Previous work assessed anonymity in a network based on the non-uniqueness of a node’s ego network. In this work, we show that this approach does not adequately account for the strong de-anonymizing effect of distant connections. We first propose the use of d-k-anonymity, a novel measure that takes knowledge up to distance d of a considered node into account. Second, we introduce anonymity-cascade, which exploits the so-called infectiousness of uniqueness: mere information about being connected to another unique node can make a given node uniquely identifiable. These two approaches, together with relevant “twin node” processing steps in the underlying graph structure, offer practitioners flexible solutions, tunable in precision and computation time. This enables the assessment of anonymity in large-scale networks with up to millions of nodes and edges. Experiments on graph models and a wide range of real-world networks show drastic decreases in anonymity when connections at distance 2 are considered. Moreover, extending the knowledge beyond the ego network with just one extra link often already decreases overall anonymity by over 50%. These findings have important implications for privacy-aware sharing of sensitive network data.

SRFA-GRL: Predicting group influence in social networks with graph representation learning

Group influence evaluation is the fundamental research in social network analysis, whose main task is evaluating the influence of the group consisting of arbitrary nodes in the social network. Many methods have been proposed to measure group influence, such as centrality-based, Monte Carlo, and path-based methods. Graph Representation Learning (GRL) has profound success in social network node-level, edge-level, and graph-level tasks. GRL integrates nodes, edges, and other information in the network structure to calculate the nodes' embeddings jointly and provide embedding features with richer information than traditional methods. Group influence is related to many factors; existing methods only focus on a single aspect but ignore the various properties. This paper proposes a Subgraph Reconstruction Feature Aggregation Graph Representation Learning based (SRFA-GRL) framework to evaluate the group influence. In the SRFA-GRL framework, a subgraph reconstruction method is proposed to capture the node distribution of the group, and a vector similarity method is proposed to compute the relative distance between the maximal connected subgraph and group embeddings. Vast experiments are conducted on eight real social networks to analyze the effectiveness of the SRFA-GRL model, and the experimental results show that the SRFA-GRL framework outperforms baseline methods.

New Cellular Learning Automata as a framework for online link prediction problem

ABSTRACT One of the main areas of research in Social Network Analysis (SNA) is Link Prediction (LP). The LP problem is useful in understanding the evolution mechanism of social networks, as well as in different applications such as recommendation systems, bioinformatics and marketing. In LP algorithms, prior network information is used to predict future connections in social networks. In this paper, we introduce a multi-wave cellular learning automaton (MWCLA) and use it to solve the LP problem in social networks. This model is a new CLA with a connected structure and a module of LAs in each cell where the neighbours of the cell module are its successors. The MWCLA method uses multiple waves at the same time in the network in order to improve convergence speed as well as accuracy. For predicting links in the social network, multiple waves can be used to consider different aspects of the network. Here we show that the model converges upon a stable and compatible configuration. Compared to some state-of-the-art approaches, MWCLA produces significantly better results when applied to the LP problem.

A Gradient Boosted Decision Tree-Based Influencer Prediction in Social Network Analysis

Twitter, Instagram and Facebook are expanding rapidly, reporting on daily news, social activities and regional or international actual occurrences. Twitter and other platforms have gained popularity because they allow users to submit information, links, photos and videos with few restrictions on content. As a result of technology advances (“big” data) and an increasing trend toward institutionalizing ethics regulation, social network analysis (SNA) research is currently confronted with serious ethical challenges. A significant percentage of human interactions occur on social networks online. In this instance, content freshness is essential, as content popularity declines with time. Therefore, we investigate how influencer content (i.e., posts) generates interactions, as measured by the number of likes and reactions. The Gradient Boosted Decision Tree (GBDT) and the Chaotic Gradient-Based Optimizer are required for estimation (CGBO). Using earlier group interactions, we develop the Influencers Prediction issue in this study’s setting of SN-created groups. We also provide a GBDT-CGBO framework and an efficient method for identifying users with the ability to influence the future behaviour of others. Our contribution is based on logic, experimentation and analytic techniques. The goal of this paper is to find domain-based social influencers using a framework that uses semantic analysis and machine learning modules to measure and predict users’ credibility in different domains and at different times. To solve these problems, future research will have to focus on co-authorship networks and economic networks instead of online social networks. The results show that our GBDT-CGBO method is both useful and effective. Based on the test results, the GBDT-CGBO model can correctly classify unclear data, which speeds up processing and makes it more efficient.

Social Network Analysis About Brand Awareness of Shopee Indonesia on Twitter

Brand recognition and brand memory are two crucial elements in brand awareness. Shopee Indonesia is one of the e-commerce in Indonesia, which is quite active on Twitter social media. This study analyzes social networks about brand awareness on Twitter through the hashtag #ShopeeID. In addition, I also want to enrich research on social network analysis in e-commerce in Indonesia. This study uses the concept of brand awareness, social media Twitter, and Social Network Analysis (SNA). The SNA research method uses the Netlytic and Gephi applications. Data collection was taken from 23-30 May 2022 for later analysis. The results showed that using hashtags in status uploads on social media could build brand awareness. The hashtag #ShopeeID can create consumer awareness of a brand as one of the e-commerce companies in Indonesia. This awareness is formed through the frequent and repeated use of hashtags by both Shopee and consumers. Shopee, a key player in disseminating information, uses Twitter as a medium to provide information through promos and giveaways. This research has implications for how e-commerce companies use hashtags in social media to create brand awareness. Businesses should pay more attention to using specific hashtags that reflect the brand than the business. When consumers can recognize a brand, that's where brand awareness is achieved.

Introduction to the special issue: Social network analysis and its application to schools.

Social network analysis (SNA) consists of a set of theories and methods that considers the direct and relationships in a clearly defined context. Although SNA research has witnessed rapid growth in the social sciences, school psychology has not kept pace. The lack of SNA studies in school psychology journals is interesting given that many topics of interest in the field both influence-and are influenced by-the social context. This special issue showcases how an array of SNA theories and methods can be used to determine the antecedents of relationships and the effects of these relationships on important outcomes. Each article was selected to highlight how SNA can shed unique insights on how social interactions can influence school aggression, friendships, language skills development, same- and cross-ethnic relationships and school belonging, and others. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Indonesian Public Response to Online Learnings During the Covid-19 Pandemic: An Analysis of Social Media

Online Learnings that were implemented during the Covid-19 pandemic invited pros and cons, especially on social media, that could be directly observed by the public. When analyzed properly, the pros and cons contain valuable information that the authorities can use to evaluate Online Learning policies. This study describes the narrative developed on social media Twitter related to Online Learning during the Covid-19 pandemic. This research includes the social network analysis (SNA) research with a descriptive qualitative approach. The platform used to collect data is Drone Emprit Academic (DEA), an SNA tool. The data analyzed relates to the number of tweets, influential actors, narrative sentiments, and robots that tweet the topic. This research does not contribute justification for right and wrong but attempts to present reliable data exposure. Based on Online Learning tweets collected on July 22-29, 2020, there were 2,903 tweets. The tweets are classified as organic because the influential actors come from personal accounts, not accounts of famous figures. Criticism of Online Learning administration is the dominant response because there is still an economic and digital divide among the public. The government needs to evaluate Online Learning policies and reconsider just education during the Covid-19 pandemic.

DynComm

The analysis of dynamics in networks represents a great deal in the social network analysis research area. To support students, teachers, developers, and researchers in this work, the authors introduce a novel R package, namely DynComm. It is designed to be a multi-language package used for community detection and analysis on dynamic networks. The package introduces interfaces to facilitate further developments and the addition of new and future developed algorithms to deal with community detection in evolving networks. This new package aims to abstract the programmatic interface of the algorithms, whether they are written in R or other languages, and expose them as functions in R.

ANALISIS PENGARUH AKTOR PADA TAGAR #ROKETCHINA DI MEDIA SOSIAL TWITTER MENGGUNAKAN SOCIAL NETWORK ANALYSIS (SNA)

The world was shocked by the news that the Chinese rocket had fallen back to earth. The news attracted the attention of the public from foreign countries to the people of Indonesia. With the presence of news on Chinese rockets, social media, one of which is Twitter, has the trending hashtag #roketchina which is widely discussed by Indonesians. This study aims to determine social networking in conversations on twitter using the hashtag #roketchina. In this study using a social network analysis research method by analyzing participant observation. The results of this study describe the communication network formed based on the hashtag #roketchina. In this network there are 12 actors and 40 ties or accounts interacting on the hashtag #roketchina. In addition, there are two types of relationships on social media twitter using the hashtag #roketchina (two modes), because communication networks are not only monitored by individuals but also official institutions or agencies. In addition, there is a Y-shaped communication network pattern, where the perpetrators have the initiative to give or even receive information about the hashtag #roketchina.

“CLIQUES”: ACADEMIC RELATIONSHIPS AMONG HIGHER EDUCATION STUDENTS

This work has investigated the academic relationships of five classes, 185 students, from an Undergraduate Business Administration course. The Social Network Analysis (SNA) research technique has been used. The results show that homophilia is predominant in the formation of groups. Only in one class does performance have a greater influence. The “cliques” were more clearly defined in only one class. The analysis of the results also indicates that students with social brokerage roles perform better than their peers.

Policy Brief 6: Bringing intelligence and social network analysis together to fight illegal wildlife trade

This policy brief explains how intelligence practitioners within law enforcement authorities and researchers skilled in social network analysis (SNA) can and should cooperate better in the fight against illegal wildlife trade (IWT). 
 Intelligence data are crucial for SNA researchers to conduct empirical analysis of illicit activities and dark networks. In turn, these research results can fruitfully feed back into investigations and further intelligence work. The combined effect of research and intelligence can have positive effects on the fight against IWT, and these effects are larger than the simple sum of their components. 
 Policymakers and stakeholders who wish to increase the effectiveness and efficiency of the fight against IWT and other environmental crimes are advised to promote the systematic cooperation between intelligence practitioners and researchers, in a way that both communities can maximise the benefits of this synergetic strategy. 
 This report was funded by PMI IMPACT, a grant award initiative of Philip Morris International (PMI). In the performance of their research, the authors maintained full independence from PMI. The views and opinions expressed in this document are those of the authors and do not necessarily reflect the views of PMI. Neither PMI, nor any of its affiliates, nor any person acting on their behalf may be held responsible for any use which may be made of the information contained herein.

Social network analysis: New ethical approaches through collective reflexivity. Introduction to the special issue of Social Networks

Research in social network analysis (SNA) faces unprecedented ethical challenges today due to both technological developments (‘big’ data) and a growing tendency toward institutionalization of ethics governance. We argue that a suitable response requires a more comprehensive approach to SNA ethics, and we identify two main paths toward this goal. First, we need to recognize the breadth of the subject, no longer limited to anonymity and consent. Second, we need to encourage the active participation and contribution of SNA scholars. We advocate a need for greater reflexivity at both individual and collective levels, whereby researchers can learn from their own and their peers’ experiences, and foster a more ethically ‘virtuous’ approach within the whole SNA community.

Research to Translation: The Healthy Schools Toolkit and New Approaches to the Whole School, Whole Community, Whole Child Model.

ABSTRACTBACKGROUNDThe Whole School, Whole Community, Whole Child (WSCC) model is an evidence‐based comprehensive framework to address health in schools. WSCC model use improves health and educational outcomes, but implementation remains a challenge.METHODSWorking with 6 schools in 2 districts in the Midwest, we used a mixed‐methods approach to determine the people, systems, and messages needed to activate WSCC implementation. We report on social network analysis and message testing findings and research translation to develop the Healthy Schools Toolkit.RESULTSSocial networks for both districts included more than 150 individuals. Both demonstrated network densities less than half of the desirable threshold, with evidence of clustering by role and minimal cross‐school relationships, posing challenges for WSCC implementation. Across stakeholder groups, messages that emphasize empathy, teamwork, and action were well‐received, especially when shared by trusted individuals through communication channels that align with stakeholder needs.CONCLUSIONSThe Healthy Schools Toolkit provides an example of a translational product that helps to bridge research with practice. With features that highlight 6 design principles, the toolkit provides complementary activities that schools and districts can use as they plan for integration of the WSCC model.

Engagement and Performance Studies of Media Agencies Publications on Social Networks

This paper presents a study that contributes to the existing work on the social diffusion and interaction strategy in social media. The aim is to know the most shared post by some electronic media in the world from end to end social network, and also to know post nature of the most successful one, and the link between different kind of interaction these are main objectives of this study. Our work is also considered as a ground and a base for social network analysis researchers in all social networks in order to allow them to benefit and help in their future research work from all information collected and results found via this study. An empirical analysis using multiple methods is conducted based on 275 Facebook publications gathered from the Facebook pages of 5 electronics journals the best one in its original country represented 5 countries in the world. This contribution discovered a set of important information and it is also projected to confirm hypothesis addressed in pre-existing studies

Examining Inter-organizational Network During Emergency Response of Gunungkidul Drought 2019

Drought is a disaster that often occurs in Gunungkidul Regency, where the condition was exacerbated by the occurrence of a long drought in 2019 (BMKG, 2019). Beside climatic and geological factors, drought is also caused by the involvement of multi-stakeholders, including local governments in managing drought emergency conditions. This research presented a case study on drought disaster management in Gunungkidul in 2019, where qualitative and social network analysis research methods were applied. Social network analysis was used to examine the central actors of the organizations involved in drought management and the role of networks in these levels of government. Meanwhile, qualitative methods were used to explore the factors influencing the network. The results show that the central actors during the emergency response were BPBD, PMI, and PDAM of Gunungkidul Regency. Meanwhile, the role of inter-organizational networks at the district level had been optimized by performing command and control over developments in the situation, involving community organizations and business institutions. Furthermore, on the factors affecting the network, everything had been maximized except two factors that were still lacking, including partnerships and the use of sophisticated information technology.

Social Network Analysis and the Law: A Proof of Concept for Argentina

Social network analysis (SNA) is a methodology to study complex systems. It has been used for many purposes: from studying the structure of English trade in the East Indies between 1601 and 1833 to the temporal structure of consensus formation in science. While its history goes back to the early twentieth century, its expansion follows the availability of computers as a way of gathering, organizing and transforming the data necessary to construct a “social network”. This paper argues that SNA is a powerful tool for studying legal systems, reviews some of the literature produced so far using it and argues that it can shed relevant insights into important legal questions. It describes the conditions necessary for successful SNA research and discusses possible ways of overcoming challenges in developing countries. It presents four preliminary approaches on Argentina: on the functioning of legislative committees in the House of Representatives, on the patterns of precedent citations in Supreme Court decisions, on the structure of the legal field in the city of Buenos Aires and on the structure of a legal advocacy clique behind a successful public interest litigation campaign.

Understanding connectivity: The parallax and disruptive-productive effects of mixed methods social network analysis in occupational science

ABSTRACT This article introduces social network analysis (SNA), a theoretical perspective accompanied by a set of methodologies, to occupational science. The convergence of SNA and occupational science is timely for both fields. By providing methodological approaches that flesh out a structural view of social networks, SNA measurements and mathematical terminology can effectively bridge the complexity of diverse interpretive frameworks used to understand occupational engagement and other constructs for humans as socially occupied beings. By focusing attention on the relationship of occupations to connectivity between agents, occupational science can make significant contributions to the ways in which the mattering or meaning of what people do with others nurtures the development and sustainability of social networks. We provide a brief history and roots of SNA in naturalistic observation, current terminology, and four widely used SNA research designs: egocentric, sociometric, sequenced, and two-mode. Drawing examples from our decade-long journey using SNA with narrative phenomenological conceptual frameworks, we illustrate how we used SNA with experience-near ethnographies to meet different objectives. In the discussion, we reflect on the parallax view created by the synergies between the disciplines and how the disruptive-productive effects that occur with mixing narrative phenomenology and SNA methods could address (mutual) methodological gaps that have seemingly limited conceptual development in the social sciences.

Capturing the participation and social dimensions of computer-supported collaborative learning through social network analysis: which method and measures matter?

The increasing use of digital learning tools and platforms in formal and informal learning settings has provided broad access to large amounts of learner data, the analysis of which has been aimed at understanding students’ learning processes, improving learning outcomes, providing learner support as well as teaching. Presently, such data has been largely accessed from discussion forums in online learning management systems and has been further analyzed through the application of social network analysis (SNA). Nevertheless, the results of these analyses have not always been reproducible. Since such learning analytics (LA) methods rely on measurement as a first step of the process, the robustness of selected techniques for measuring collaborative learning activities is critical for the transparency, reproducibility and generalizability of the results. This paper presents findings from a study focusing on the validation of critical centrality measures frequently used in the fields of LA and SNA research. We examined how different network configurations (i.e., multigraph, weighted, and simplified) influence the reproducibility and robustness of centrality measures as indicators of student learning in CSCL settings. In particular, this research aims to contribute to the provision of robust and valid methods for measuring and better understanding of the participation and social dimensions of collaborative learning. The study was conducted based on a dataset of 12 university courses. The results show that multigraph configuration produces the most consistent and robust centrality measures. The findings also show that degree centralities calculated with the multigraph methods are reliable indicators for students’ participatory efforts as well as a consistent predictor of their performance. Similarly, Eigenvector centrality was the most consistent centrality that reliably represented social dimension, regardless of the network configuration. This study offers guidance on the appropriate network representation as well as sound recommendations about how to reliably select the appropriate metrics for each dimension.