基于改进的Vicsek模型的社会网络同步聚类算法

Synchronous Clustering Algorithm for Social Networks Based on Improved Vicsek Model

【目的】设计基于改进的Vicsek模型的同步聚类算法,研究社会网络的同步演化过程与簇结构。【方法】针对原始Vicsek模型的个体运动速率恒定问题,引入速率自我调节规则调整个体演化速率;针对原始Vicsek模型的个体重要性相同问题,引入个体重要性控制个体演化方向。【结果】利用金融网络数据集验证本文算法,F1-Score高于Sync算法和基于Vicsek模型的聚类算法。【局限】算法时间复杂度与数据集规模成正相关关系,使得算法时间复杂度较高。【结论】基于改进的Vicsek模型的同步聚类算法能较好地刻画复杂社会网络的演化与同步过程,准确发现社会网络中的簇结构。

[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 selfregulation 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.