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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\u2019 social strength is proposed. The model is based on students\u2019 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\u2019 education management.<\/jats:p>","DOI":"10.3233\/ida-216318","type":"journal-article","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T11:17:22Z","timestamp":1675163842000},"page":"137-163","source":"Crossref","is-referenced-by-count":2,"title":["Inferring student social link from spatiotemporal behavior data via entropy-based analyzing model"],"prefix":"10.1177","volume":"27","author":[{"given":"Mengran","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuanqi","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-216318_ref2","doi-asserted-by":"crossref","unstructured":"M. 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