Seeding the kernels in graphs: Toward multi-resolution community analysis

Jie Zhang, Kai Zhang, Xiao Ke Xu, Chi Kong Tse, Michael Small

Research output: Journal article publicationJournal articleAcademic researchpeer-review

37 Citations (Scopus)


Current 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.
Original languageEnglish
Article number113003
JournalNew Journal of Physics
Publication statusPublished - 2 Nov 2009

ASJC Scopus subject areas

  • General Physics and Astronomy


Dive into the research topics of 'Seeding the kernels in graphs: Toward multi-resolution community analysis'. Together they form a unique fingerprint.

Cite this