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Knowledge and Information Systems

, Volume 60, Issue 2, pp 757–779 | Cite as

Community detection using multilayer edge mixture model

  • Han Zhang
  • Chang-Dong WangEmail author
  • Jian-Huang Lai
  • Philip S. Yu
Regular Paper

Abstract

Multilayer networks are networks where edges exist in multiple layers that encode different types or sources of interactions. As one of the most important problems in network science, discovering the underlying community structure in multilayer networks has received an increasing amount of attention in recent years. One of the challenging issues is to develop effective community structure quality functions for characterizing the structural or functional properties of the expected community structure. Although several quality functions have been developed for evaluating the detected community structure, little has been explored about how to explicitly bring our knowledge of the desired community structure into such quality functions, in particular for the multilayer networks. To address this issue, we propose the multilayer edge mixture model (MEMM), which is positioned as a general framework that enables us to design a quality function that reflects our knowledge about the desired community structure. The proposed model is based on a mixture of the edges, and the weights reflect their role in the detection process. By decomposing a community structure quality function into the form of MEMM, it becomes clear which type of community structure will be discovered by such quality function. Similarly, after such decomposition we can also modify the weights of the edges to find the desired community structure. In this paper, we apply the quality functions modified with the knowledge of MEMM to different multilayer benchmark networks as well as real-world multilayer networks and the detection results confirm the feasibility of MEMM.

Keywords

Community detection Multilayer network General quality function Edge mixture 

Notes

Acknowledgements

This work was supported by NSFC (61502543 and 61672313), National Key Research and Development Program of China (2016YFB1001003), Guangdong Natural Science Funds for Distinguished Young Scholar (2016A030306014), Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (2016TQ03X542), NSF through Grants IIS-1526499, IIS-1763325, and CNS-1626432.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Han Zhang
    • 1
  • Chang-Dong Wang
    • 1
    Email author
  • Jian-Huang Lai
    • 1
  • Philip S. Yu
    • 2
  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouPeople’s Republic of China
  2. 2.University of Illinois at ChicagoChicagoUSA

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