Abstract
During the recent years, community detection in complex network has become a hot research topic in various research fields including mathematics, physics and biology. Identifying communities in complex networks can help us to understand and exploit the networks more clearly and efficiently. In this paper, we investigate the topological structure of complex networks and propose a novel method for community detection in complex network, which owns several outstanding properties, such as efficiency, robustness, broad applicability and semantic. The method is based on partitioning vertex and degree entropy, which are both proposed in this paper. Partitioning vertex is a novel efficient representation for communities and degree entropy is a new measure for the results of community detection. We apply our method to several large-scale data-sets which are up to millions of edges, and the experimental results show that our method has good performance and can find the community structure hidden in complex networks.
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Yiwen, W., Min, Y. (2012). A Novel Method for Community Detection in Complex Network Using New Representation for Communities. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds) New Frontiers in Applied Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 7104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28320-8_8
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DOI: https://doi.org/10.1007/978-3-642-28320-8_8
Publisher Name: Springer, Berlin, Heidelberg
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