# Visual Analytic Method for Students' Association via Modularity Optimization
###### Abstract  
Students spend most of their time living and studying on campus, especially in Asia, and they form various types of associations in addition to those with classmates and roommates. It is necessary for university authorities to master these types of associations, so as to provide appropriate services, such as psychological guidance and academic advice. With the rapid development of the \"smart campus,\" many kinds of student behavior data are recorded, which provides an unprecedented opportunity to deeply analyze students' associations. In this paper, we propose a visual analytic method to construct students' association networks by computing the similarity of their behavior data. We discover student communities using the popular Louvain (or BGLL) algorithm, which can extract community structures based on modularity optimization. Using various visualization charts, we visualized associations among students so as to intuitively express them. We evaluated our method using the real behavior data of undergraduates in a university in Beijing. The experimental results indicate that this method is effective and intuitive for student association analysis. Keywords:behavior data; Louvain algorithm; modularity optimization; students association; visualization +
Footnote †: journal: Medical  
and forecasting career choices [8]. These works showed that it is possible to analyze students' lives through their behavior data. To accurately and intuitively analyze associations, we propose a visual analytic method in which an association network is constructed based on the similarity of students' behavior. However, with a growing number of students, the association network becomes too complex to clearly observe. To overcome this issue, we use the Louvain algorithm [9], a kind of mostly adopted community detection algorithm based on modularity optimization, hierarchically partition the association network into communities. The students belonging to one community have higher probability of being friends with each other than with students of other communities. Identifying student communities offers insight on how the association network is organized. It allows us to focus on regions of interest and helps to classify the students based on their role with respect to the communities they belong to. For example, we can distinguish students who are embedded within the community from students at the boundary of the community, the two types of students play different roles in the community and may need different attention. Finally, we use visualization charts to express these associations intuitively. The main contributions and advantages of our method are as follows. * The multi-source behavior data of students are collected from different information management systems, and then they are fused to construct an activity sequence for each student, which is the basis for analysis of association among students. * Three kinds of similarity operators are proposed to compute the degree of similarity of behavior data among students. We take the similarity value as a weight of edges to generate association network representing social ties among students. * The student communities were discovered by adopting the Louvain algorithm in the association network. We can further understand the organizational structure of association network through the hierarchical community structure. * A visual analytic system was developed that interactively exhibits the association among students and allows us to intuitively explore the social relationships of specific students or communities of interest. The remainder of this paper is organized as follows. Section 2 reviews the related work from the three aspects of how to infer social ties from spatiotemporal behavior data, detect communities from a complex network, and apply visualization techniques to detect communities. Section 3 introduces the framework of our method. Section 4 highlights privacy issues. Section 5 describes the experimental dataset and seven extracted behavior features.