Detecting genuine communities from large-scale social networks: A pattern-based method

被引：0

作者：

机构：

[1] Wu, Zhiang

[2] 1,Cao, Jie

[3] Wu, Junjie

[4] Wang, Youquan

[5] Liu, Chunyang

来源：

Liu, C. (lcy@isc.org.cn) | 1600年 / Oxford University Press卷 / 57期

关键词：

Community detection is a long-standing yet very difficult task in social network analysis. It becomes more challenging as many online social networking sites are evolving into super-large scales. Numerous methods have been proposed for community detection from massive networks; but how to reconcile the partitioning efficiency and the community quality remains an open problem. In this paper; we attempt to address this challenge by introducing a COSine-pattern-based COMmunity extraction framework: COSCOM.TheCOSCOMadopts an extracting view of community detection. It first extracts the so-called asymptotically equivalent structures (AESs) from networks; from which the nodes are further partitioned into crisp communities using any of the existing methods. Specifically; we prove that anAESis a very tight group of nodes; and is actually a cosine pattern defined by the extended cosine similarity.A novel cosine-pattern mining algorithm based on the ordered antimonotone of cosine similarity is thus proposed for the efficient extraction of AESs. Experiments on various real-world social networks demonstrate the advantage of the extracting view of community detection. In particular; COSCOM shows merits in detecting genuine communities by either internal or external validity. © The British Computer Society 2013;

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