Overlap community detection using spectral algorithm based on node convergence degree

Weimin Li, Shu Jiang, Qun Jin

    Research output: Contribution to journalArticle

    6 Citations (Scopus)

    Abstract

    Community structure is a typical feature of complex networks in cyberspace, and community detection is considered to be crucial to understanding the topology structure, network function and social dynamics of cyberspace. However, some particular nodes may simultaneously belong to several communities in cyberspace. Though there are many algorithms to detect the overlapping communities, most of them are based on the network structure without considering the attributes of the nodes. In this paper, we focus on the convergence characteristic of network and propose an overlap community detection algorithm based on the node convergence degree, which is defined as a combination of attribute convergence degree and structure convergence degree. It combines the network topology with the attributes of the nodes and considers both local and global information of a node. An improved PageRank algorithm is used to get the importance of each node in the global network, while the information of local network is used to measure the structure convergence degree. The overlap communities are thus identified by spectral cluster based on the node convergence degree. Finally, experiment results demonstrate the effectiveness and better performance of our proposed method.

    Original languageEnglish
    JournalFuture Generation Computer Systems
    DOIs
    Publication statusAccepted/In press - 2017

    Fingerprint

    Topology
    Complex networks
    Experiments

    Keywords

    • Community structure
    • Node convergence degree
    • Overlap
    • PageRank

    ASJC Scopus subject areas

    • Software
    • Hardware and Architecture
    • Computer Networks and Communications

    Cite this

    Overlap community detection using spectral algorithm based on node convergence degree. / Li, Weimin; Jiang, Shu; Jin, Qun.

    In: Future Generation Computer Systems, 2017.

    Research output: Contribution to journalArticle

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