Dynamic evolutionary community detection algorithms based on the modularity matrix

Motivated by the relationship of the dynamic behaviors and network structure, in this paper, we present two efficient dynamic community detection algorithms. The phases of the nodes in the network can evolve according to our proposed differential equations. In each iteration, the phases of the nodes...

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Published inChinese physics B Vol. 23; no. 11; pp. 686 - 691
Main Author 陈建芮 洪志敏 汪丽娜 乌兰
Format Journal Article
LanguageEnglish
Published 01.11.2014
Subjects
Algorithms
Communities
Computer simulation
Differential equations
Dynamics
Evolution
Modularity
Networks
Phases
动态演化
动态行为
检测结果
模块化
测算法
矩阵
社区
网络结构
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ISSN1674-1056
2058-3834
1741-4199
DOI10.1088/1674-1056/23/11/118903

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Summary:Motivated by the relationship of the dynamic behaviors and network structure, in this paper, we present two efficient dynamic community detection algorithms. The phases of the nodes in the network can evolve according to our proposed differential equations. In each iteration, the phases of the nodes are controlled by several parameters. It is found that the phases of the nodes are ultimately clustered into several communities after a short period of evolution. They can be adopted to detect the communities successfully. The second differential equation can dynamically adjust several parameters, so it can obtain satisfactory detection results. Simulations on some test networks have verified the efficiency of the presented algorithms.
Bibliography:community detection, dynamic evolutionary, modularity matrix, synchronization
Chen Jian-Rui, Hung Zhi-Min, Wang Li-Na, and Wu Lan（ College of Science, Inner Mongolia University of Technology, Hohhot 010051, China）
11-5639/O4
Motivated by the relationship of the dynamic behaviors and network structure, in this paper, we present two efficient dynamic community detection algorithms. The phases of the nodes in the network can evolve according to our proposed differential equations. In each iteration, the phases of the nodes are controlled by several parameters. It is found that the phases of the nodes are ultimately clustered into several communities after a short period of evolution. They can be adopted to detect the communities successfully. The second differential equation can dynamically adjust several parameters, so it can obtain satisfactory detection results. Simulations on some test networks have verified the efficiency of the presented algorithms.
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ISSN:1674-1056
2058-3834
1741-4199
DOI:10.1088/1674-1056/23/11/118903