Information flow simulation community detection of weighted-directed campus friendship network in continuous time
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ID: 282117
2023
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Abstract
Educational data mining has become an important research field in studying
the 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 a
new approach to address these limitations by reconstructing the campus
friendship network into weighted directed networks in continuous time,
improving the effectiveness of traditional campus friendship network and the
accuracy of community detection results. To achieve this, a new weighted
directed community detection algorithm for campus friendship network in
continuous time is proposed, and it is used to study the community detection of
a university student. The results show that the weighted directed friendship
network reconstructed in this paper can reveal the real friend relationships
better than the initial undirected unauthorized friendship network.
Furthermore, the community detection algorithm proposed in this paper obtains
better community detection effects. After community detection, students in the
same community exhibit similarities in consumption level, eating habits, and
behavior regularity. This paper enriches the theoretical research of complex
friendship network considering the characteristics of time, and also provides
objective scientific guidance for the management of college students.
| Reference Key |
menghui2023information
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|---|---|
| Authors | Ren Chao; Yang Menghui |
| Journal | arXiv |
| Year | 2023 |
| DOI |
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| URL | |
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