Inferring student social link from spatiotemporal behavior data via entropy-based analyzing model

Abstract

Social link is an important index to understand master students’ mental health and social ability in educational management. Extracting hidden social strength from students’ rich daily life behaviors has also become an attractive research hotspot. Devices with positioning functions record many students’ spatiotemporal behavior data, which can infer students’ social links. However, under the guidance of school regulations, students’ daily activities have a certain regularity and periodicity. Traditional methods usually compare the co-occurrence frequency of two users to infer social association but do not consider the location-intensive and time-sensitive in campus scenes. Aiming at the campus environment, a Spatiotemporal Entropy-Based Analyzing (S-EBA) model for inferring students’ social strength is proposed. The model is based on students’ multi-source heterogeneous behavioral data to calculate the frequency of co-occurrence under the influence of time intervals. Then, the three features of diversity, spatiotemporal hotspot and behavior similarity are introduced to calculate social strength. Experiments show that our method is superior to the traditional methods under many evaluating criteria. The inferred social strength is used as the weight of the edge to construct a social network further to analyze its important impact on students’ education management.