Jie Zhang et al 2009 New J. Phys. 11 113003 doi:10.1088/1367-2630/11/11/113003
Seeding the Kernels in graphs: toward multi-resolution community analysis
Jie Zhang1,4, Kai Zhang2, Xiao-ke Xu1,3, Chi K Tse1 and Michael Small1
Show affiliationsCurrent endeavors in community detection suffer from the resolution limit problem and can be quite expensive for large networks, especially those based on optimization schemes. We propose a conceptually different approach for multi-resolution community detection, by introducing the kernels from statistical literature into the graph, which mimic the node interaction that decays locally with the geodesic distance. The modular structure naturally arises as the patterns inherent in the interaction landscape, which can be easily identified by the hill climbing process. The range of node interaction, and henceforth the resolution of community detection, is controlled via tuning the kernel bandwidth in a systematic way. Our approach is computationally efficient and its effectiveness is demonstrated using both synthetic and real networks with multiscale structures.
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Issue 11 (November 2009)
Received 24 August 2009
Published 2 November 2009
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Seeding the Kernels in graphs: toward multi-resolution community analysis
Jie Zhang et al 2009 New J. Phys. 11 113003
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