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Educational data mining has become an important research field in studyingthe social behavior of college students using massive data. However,traditional campus friendship network and their community detection algorithms,which lack time characteristics, have their limitations. This paper proposes anew approach to address these limitations by reconstructing the campusfriendship network into weighted directed networks in continuous time,improving the effectiveness of traditional campus friendship network and theaccuracy of community detection results. To achieve this, a new weighteddirected community detection algorithm for campus friendship network incontinuous time is proposed, and it is used to study the community detection ofa university student. The results show that the weighted directed friendshipnetwork reconstructed in this paper can reveal the real friend relationshipsbetter than the initial undirected unauthorized friendship network.Furthermore, the community detection algorithm proposed in this paper obtainsbetter community detection effects. After community detection, students in thesame community exhibit similarities in consumption level, eating habits, andbehavior regularity. This paper enriches the theoretical research of complexfriendship network considering the characteristics of time, and also providesobjective scientific guidance for the management of college students.

Information flow simulation community detection of weighted-directed campus friendship network in continuous time