Research On Dual Evolution Pattern Mining Model For Time-Varying Community Detection In Dynamic Networks

YuLe Zhang; HongYan Li; Wei Yu; RuJing Liu; YingJie Xu; RuoYu Hu; ShiHong Wu

doi:10.63313/JCSFT.9047

Authors

YuLe Zhang College of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, China Author
HongYan Li College of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, China Author
Wei Yu College of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, China Author
RuJing Liu College of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, China Author
YingJie Xu College of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, China Author
RuoYu Hu College of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, China Author
ShiHong Wu College of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, China Author

DOI:

https://doi.org/10.63313/JCSFT.9047

Keywords:

Community testing, Non-negative matrix factorization, Time evolution matrix, Dual evolution

Abstract

Nowadays, more and more groups are studying community detection. Community detection also has many practical applications in real life, such as: modeling the brain in the field of neuroscience, effectively inferring relationships and preferences between users in social networks, etc. At present, most teams use time snapshots to simulate the dynamic community network, which ignores the small error caused by the change of time between each time snapshot. Therefore, in order to simulate the influence of time on this clique network, we add two time evolution matrices corresponding to both the sum matrix and the system matrix, and propose our new evolution model: double evolutionary pattern mining model for time-varying community detection in dynamic networks. At the same time we find that finally, the experimental results on the actual network show that the proposed algorithm has higher prediction accuracy in dynamic networks compared with other algorithms.

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