Affiliations: [a] Information Technology Center, Beijing University of Technology, Beijing, China | [b] Academic Affairs Office, Beijing Open University, Beijing, China | [c] Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing Artificial Intelligence Institute, Beijing University of Technology, Beijing, China | [d] State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Correspondence:
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Corresponding author: Huimin Cheng, Information Technology Center, Beijing University of Technology, Beijing, China. E-mail: chenghm@bjut.edu.cn.
Abstract: Social relationships among students at campus are closely related to their mental health and academic performance. Therefore, it is a very important task for educators to analyze students’ social relationships. However, existing studies have focused on one-to-one social relationships between students, few ones have explored the high-order community relationships hidden in social networks, especially in a visual manner. To solve this problem, a visual analysis system called ViSSR is proposed in this paper, which utilizes the Louvain algorithm to detect the hierarchical community structure of students’ social network at campus, and then provides four coordinated views to visualize the detection results. Among the views, the hierarchical hypergraph view is to visualize the hierarchical community structure that greatly breaks through the limitations of first-order relationships available in a traditional node-link social network, the community analysis view and individual analysis view show the social characteristics of a community and individual student respectively, and the matrix view displays the behavioral features of students. Case studies and experts evaluation have been conducted to demonstrate the usability of the system.
Keywords: Student social relationships, visual analysis, community detection, hierarchical hypergraph
