中国物理B ›› 2013, Vol. 22 ›› Issue (5): 58903-058903.doi: 10.1088/1674-1056/22/5/058903

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

A thermal flux-diffusing model for complex networks and its applications in community structure detection

沈毅   

  1. College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
  • 收稿日期:2012-06-04 修回日期:2012-10-19 出版日期:2013-04-01 发布日期:2013-04-01
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No. 60672095), the Fundamental Research Funds for the Central Universities, China (Grant No. KYZ201300), and the Youth Sci-Tech Innovation Fund of Nanjing Agricultural University, China (Grant No. KJ2010024).

A thermal flux-diffusing model for complex networks and its applications in community structure detection

Shen Yi (沈毅)   

  1. College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
  • Received:2012-06-04 Revised:2012-10-19 Online:2013-04-01 Published:2013-04-01
  • Contact: Shen Yi E-mail:shen_yi1979@njau.edu.cn
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No. 60672095), the Fundamental Research Funds for the Central Universities, China (Grant No. KYZ201300), and the Youth Sci-Tech Innovation Fund of Nanjing Agricultural University, China (Grant No. KJ2010024).

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摘要： We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature distribution of nodes in time that scales linearly with the network size. Then, the local community enclosing a given node can be easily detected for the reason that the dense connections in the local communities lead to the temperatures of nodes in the same community being close to each other. The community structure of a network can be recursively detected by randomly choosing the nodes outside the detected local communities. In the experiments, we apply our method to a set of benchmarking networks with known pre-determined community structures. The experiment results show that our method has higher accuracy and precision than most existing globe methods and is better than the other existing local methods in the selection of the initial node. Finally, several real-world networks are investigated.

关键词: complex networks, community structure, thermal flux-diffusing model

Abstract: We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature distribution of nodes in time that scales linearly with the network size. Then, the local community enclosing a given node can be easily detected for the reason that the dense connections in the local communities lead to the temperatures of nodes in the same community being close to each other. The community structure of a network can be recursively detected by randomly choosing the nodes outside the detected local communities. In the experiments, we apply our method to a set of benchmarking networks with known pre-determined community structures. The experiment results show that our method has higher accuracy and precision than most existing globe methods and is better than the other existing local methods in the selection of the initial node. Finally, several real-world networks are investigated.

Key words: complex networks, community structure, thermal flux-diffusing model

中图分类号:  (Networks and genealogical trees)

  • 89.75.Hc
89.75.Da (Systems obeying scaling laws)