World Wide Web

, Volume 18, Issue 5, pp 1373–1390 | Cite as

Detecting overlapping communities in poly-relational networks

  • Zhiang Wu
  • Jie Cao
  • Guixiang Zhu
  • Wenpeng Yin
  • Alfredo Cuzzocrea
  • Jin Shi


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.


Social networks Community detection Poly-relational networks MutuRank Gaussian mixture model 



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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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Zhiang Wu
    • 1
  • Jie Cao
    • 1
  • Guixiang Zhu
    • 1
  • Wenpeng Yin
    • 2
  • Alfredo Cuzzocrea
    • 3
  • Jin Shi
    • 4
  1. 1.Jiangsu Provincial Key Laboratory of E-BusinessNanjing University of Finance and EconomicsNanjingChina
  2. 2.University of MunichMunichGermany
  3. 3.Institute of High Performance Computing and NetworkingItalian National Research CouncilRomeItaly
  4. 4.School of Information ManagementNanjing UniversityNanjingChina

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