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Detecting overlapping communities in poly-relational networks

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Abstract

Discovering communities can promote the understanding of the structure, function and evolution in various systems. Overlapping community detection in poly-relational networks has gained much more interests in recent years, due to the fact that poly-relational networks and communities with pervasive overlap are prevalent in the real world. A plethora of methods detect communities from the poly-relational network by converting it to mono-relational networks first. Nevertheless, they commonly assume different relations are independent from each other, which is obviously unreal to real-life cases. In this paper, we attempt to relax this strong assumption by introducing a novel co-ranking framework, named MutuRank. It makes full use of the mutual influence between relations and actors to transform the poly-relational network to the mono-relational network. We then present a novel GMM-NK (Gaussian Mixture Model with Neighbor Knowledge) algorithm incorporating the impact from neighbors into the traditional GMM. Experimental results both on synthetic networks and the real-world network have verified the effectiveness of MutuRank and GMM-NK.

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Notes

  1. http://webdocs.cs.ualberta.ca/~zaiane/htmldocs/ConfRanking.html

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Acknowledgments

This research was partially supported by National Natural Science Foundation of China under Grants 61103229, 71372188 and 61100197, National Center for International Joint Research on E-Business Information Processing under Grant 2013B01035, National Key Technologies R&D Program of China under Grant 2013BAH16F01, Industry Projects in Jiangsu S&T Pillar Program under Grant BE2014141, Key Project of Natural Science Research in Jiangsu Provincial Colleges and Universities under Grants 12KJA520001 and 14KJA520001, National Soft Science Research Program under Grant 2013GXS4B081 and the Natural Science Foundation of Jiangsu Province of China under Grant BK2012863.

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Authors and Affiliations

  1. Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing, China

    Zhiang Wu, Jie Cao & Guixiang Zhu

  2. University of Munich, Munich, Germany

    Wenpeng Yin

  3. Institute of High Performance Computing and Networking, Italian National Research Council, Rome, Italy

    Alfredo Cuzzocrea

  4. School of Information Management, Nanjing University, Nanjing, China

    Jin Shi

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  1. Zhiang Wu
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  2. Jie Cao
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  3. Guixiang Zhu
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  4. Wenpeng Yin
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  5. Alfredo Cuzzocrea
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  6. Jin Shi
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Correspondence to Zhiang Wu.

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Wu, Z., Cao, J., Zhu, G. et al. Detecting overlapping communities in poly-relational networks. World Wide Web 18, 1373–1390 (2015). https://doi.org/10.1007/s11280-015-0330-8

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