Author:
Li Mengran,Zhang Yong,Li Xiaoyong,Lin Xuanqi,Yin Baocai
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.
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Theoretical Computer Science
Reference29 articles.
1. M. Backes, M. Humbert, J. Pang and Y. Zhang, walk2friends: Inferring social links from mobility profiles, in: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 2017, pp. 1943–1957.
2. Fast unfolding of communities in large networks;Blondel;Journal of Statistical Mechanics: Theory and Experiment,2008
3. The social ties that bind: Social anxiety and academic achievement across the university years;Brook;Journal of Youth and Adolescence,2015
4. Inferring social ties from geographic coincidences;Crandall;Proceedings of the National Academy of Sciences,2010
5. Constructing activity-mobility trajectories of college students based on smart card transaction data;Ebadi;International Journal of Transportation Science and Technology,2017
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Optimization and Performance Analysis of Particle Swarm Optimization Algorithm in Learning Behavior System;2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT);2023-10-20