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A Unified Model for Community Detection of Multiplex Networks

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Web Information Systems Engineering – WISE 2014 (WISE 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8786))

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Abstract

Multiplex networks contain multiple simplex networks. Community detection of multiplex networks needs to deal with information from all the simplex networks. Most approaches aggregate all the links in different simplex networks treating them as being equivalent. However, such aggregation might ignore information of importance in simplex networks. In addition, for each simplex network, the aggregation only considers adjacency relation among nodes, which can’t reflect real closeness among nodes very well. In order to solve the problems above, this paper presents a unified model to detect community structure by grouping the nodes based on a unified matrix transferred from multiplex network. In particular, we define importance and node similarity to describe respectively correlation difference of simplex networks and closeness among nodes in each simplex network. The experiment results show the higher accuracy of our model for community detection compared with competing methods on synthetic datasets and real world datasets.

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Author information

Authors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Guangyao Zhu & Kan Li

Authors
  1. Guangyao Zhu
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  2. Kan Li
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Editor information

Editors and Affiliations

  1. University of New South Wales, Sydney, Australia

    Boualem Benatallah

  2. Boston University, Boston, MA, USA

    Azer Bestavros

  3. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Yannis Manolopoulos  & Athena Vakali  & 

  4. Victoria University, Footscray, VIC, Australia

    Yanchun Zhang

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Zhu, G., Li, K. (2014). A Unified Model for Community Detection of Multiplex Networks. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8786. Springer, Cham. https://doi.org/10.1007/978-3-319-11749-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-11749-2_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11748-5

  • Online ISBN: 978-3-319-11749-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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