Finding College Student Social Networks by Mining the Records of Student ID Transactions

Abstract

Information about college students’ social networks plays a pivotal role in college students’ mental health monitoring and student management. While there have been many studies to infer social networks by data mining, the mining of college students’ social networks lacks consideration of homophily. College students’ social behaviors show significant homophily in the aspect of major and grade. Consequently, the inferred inter-major and inter-grade social ties will be erroneously omitted without considering such an effect. In this work, we aimed to increase the fidelity of the extracted networks by alleviating the homophily effect. To achieve this goal, we propose a method that combines the sliding time-window method with the hierarchical encounter model based on association rules. Specifically, we first calculated the counts of spatial–temporal co-occurrences of each student pair. The co-occurrences were acquired by the sliding time-window method, which takes advantage of the symmetry of the social ties. We then applied the hierarchical encounter model based on association rules to extract social networks by layer. Furthermore, we propose an adaptive method to set co-occurrence thresholds. Results suggested that our model infers the social networks of students with better fidelity, with the proportion of extracted inter-major social ties in entire social ties increasing from 0.89% to 5.45% and the proportion of inter-grade social ties rising from 0.92% to 4.65%.

Resonance - An Intelligence Analysis Framework for Social Connection Inference via Mining Co-Occurrence Patterns Over Multiplex Trajectories

“…Xu et al [30] calculate the counts of spatio-temporal cooccurrences of each student pair based on the transactions of students' campus cards. Instead of a fixed time-slicing method, the researchers acquire the co-occurrences events by using a sliding time-window method.…”

“…Instead of using fixed time slices, inspired by the notion of the sliding window, we come up with a sliding prism approach to group trajectories into different resonance prisms for further analysis. The approach manages to avoid the event loss issues [30].…”

Visual Analytic Method for Students’ Association via Modularity Optimization

“…A day was divided into 144 10-min bins. Behavior time was therefore encoded as a number in the range [1,144], and we could not obtain the precise behavior time. Verification was performed by the student services department; they invited some students as volunteers to verify the experimental results.…”

“…These data describe students' daily behavior on campus from many aspects, which makes the multi-source behavior data available for in-depth analysis of their associations. Previous works based on behavior data has addressed topics such as constructing student social networks [1,2], predicting academic performance [3][4][5][6][7], and forecasting career choices [8]. These works showed that it is possible to analyze students' lives through their behavior data.…”

e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

“…There is no unified measurement to evaluate the usefulness of rules. The traditional support-confidence framework is not applicable to mine HUNSR because it does not involve the utility measure [16][17][18].Furthermore, the utility-confidence framework in high utility association rule (HUAR) mining is not applicable either because it does not involve the ordinal nature of sequential patterns [19]. So, it is very important to formalize the problem properly and comprehensively.…”