›› 2014, Vol. 23 ›› Issue (11): 118903-118903.doi: 10.1088/1674-1056/23/11/118903

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇    下一篇

Dynamic evolutionary community detection algorithms based on the modularity matrix

陈建芮, 洪志敏, 汪丽娜, 乌兰   

  1. College of Science, Inner Mongolia University of Technology, Hohhot 010051, China
  • 收稿日期:2014-01-31 修回日期:2014-06-01 出版日期:2014-11-15 发布日期:2014-11-15
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No. 61272279), the TianYuan Special Funds of the National Natural Science Foundation of China (Grant No. 11326239), the Higher School Science and Technology Research Project of Inner Mongolia, China (Grant No. NJZY13119), and the Inner Mongolia University of Technology, China (Grant No. ZD201221).

Dynamic evolutionary community detection algorithms based on the modularity matrix

Chen Jian-Rui (陈建芮), Hong Zhi-Min (洪志敏), Wang Li-Na (汪丽娜), Wu Lan (乌兰)   

  1. College of Science, Inner Mongolia University of Technology, Hohhot 010051, China
  • Received:2014-01-31 Revised:2014-06-01 Online:2014-11-15 Published:2014-11-15
  • Contact: Chen Jian-Rui E-mail:jianrui_chen@sina.com
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No. 61272279), the TianYuan Special Funds of the National Natural Science Foundation of China (Grant No. 11326239), the Higher School Science and Technology Research Project of Inner Mongolia, China (Grant No. NJZY13119), and the Inner Mongolia University of Technology, China (Grant No. ZD201221).

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摘要： 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.

关键词: community detection, dynamic evolutionary, modularity matrix, synchronization

Abstract: 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.

Key words: community detection, dynamic evolutionary, modularity matrix, synchronization

中图分类号:  (Structures and organization in complex systems)

  • 89.75.Fb
89.75.Hc (Networks and genealogical trees) 84.30.Bv (Circuit theory)