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Graph kernels, hierarchical clustering, and network community structure: experiments and comparative analysis

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  • S. Zhang
  • X.-M. Ning
  • X.-S. Zhang

Abstract

There has been a quickly growing interest in properties of complex networks, such as the small world property, power-law degree distribution, network transitivity, and community structure, which seem to be common to many real world networks. In this study, we consider the community property which is also found in many real networks. Based on the diffusion kernels of networks, a hierarchical clustering approach is proposed to uncover the community structure of different extent of complex networks. We test the method on some networks with known community structures and find that it can detect significant community structure in these networks. Comparison with related methods shows the effectiveness of the method. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2007

Suggested Citation

  • S. Zhang & X.-M. Ning & X.-S. Zhang, 2007. "Graph kernels, hierarchical clustering, and network community structure: experiments and comparative analysis," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 57(1), pages 67-74, May.
    • Handle: RePEc:spr:eurphb:v:57:y:2007:i:1:p:67-74
    DOI: 10.1140/epjb/e2007-00146-y
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    Cited by:

    1. Xiang, Ju & Tang, Yan-Ni & Gao, Yuan-Yuan & Zhang, Yan & Deng, Ke & Xu, Xiao-Ke & Hu, Ke, 2015. "Multi-resolution community detection based on generalized self-loop rescaling strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 127-139.

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