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Synchronous Clustering Algorithm for Social Networks Based on Improved Vicsek Model |
Yang Xu1,Qian Xiaodong2() |
1 School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China 2 School of Economics and Management, Lanzhou Jiaotong University, Lanzhou 730070, China |
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Abstract [Objective] The paper designs an algorithm based on the improved Vicsek model, aiming to study the synchronous evolution process and cluster structure of social networks. [Methods] First, we introduced a rate self-regulation rule to adjust the individual evolution rate of the original Vicsek model. Then, we used individual importance to control the direction of individual evolution of the Vicsek model. [Results] We examined our new algorithm with datasets of financial networks. The F1-Score for clustering results was higher than the Sync algorithm and clustering algorithm based on the original Vicsek model. [Limitations] The clustering time was very complex with large datasets. [Conclusions] The proposed algorithm could effectively describe the evolution and synchronization of complex social networks, and then accurately discover their cluster structures.
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Received: 11 June 2019
Published: 01 June 2020
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Corresponding Authors:
Qian Xiaodong
E-mail: qianxd@mail.lzjtu.cn
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