Scalably revealing the dynamics of soft community structure in complex networks

被引:0
作者
Huijia Li
Huiying Li
机构
[1] Central University of Finance and Economics,School of Management Science and Engineering
[2] Tsinghua University,Department of Automation
来源
Journal of Systems Science and Complexity | 2016年 / 29卷
关键词
Community detection; dynamical behavior; Markov process; Potts model; soft partition;
D O I
暂无
中图分类号
学科分类号
摘要
Revealing the dynamics of community structure is of great concern for scientists from many fields. Specifically, how to quantify the dynamic details of soft community structure is a very interesting topic. In this paper, the authors propose a novel framework to study the scalable dynamic behavior of the soft community structure. First, the authors model the Potts dynamics to detect community structure using a “soft” Markov process. Then the soft stability of in a multiscale view is proposed to naturally uncover the local uniform behavior of spin values across multiple hierarchical levels. Finally, a new partition index is developed to detect fuzzy communities based on the stability and the dynamical information. Experiments on the both synthetically generated and real-world networks verify that the framework can be used to uncover hierarchical community structures effectively and efficiently.
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收藏
页码:1071 / 1088
页数:17
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