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Community Detection for Multiplex Social Networks Based on Relational Bayesian Networks

  • Jiuchuan Jiang
  • Manfred Jaeger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)

Abstract

Many techniques have been proposed for community detection in social networks. Most of these techniques are only designed for networks defined by a single relation. However, many real networks are multiplex networks that contain multiple types of relations and different attributes on the nodes. In this paper we propose to use relational Bayesian networks for the specification of probabilistic network models, and develop inference techniques that solve the community detection problem based on these models. The use of relational Bayesian networks as a flexible high-level modeling framework enables us to express different models capturing different aspects of community detection in multiplex networks in a coherent manner, and to use a single inference mechanism for all models.

Keywords

Community detection Multiplex networks Relational Bayesian networks Statistical relational learning 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jiuchuan Jiang
    • 1
  • Manfred Jaeger
    • 1
  1. 1.Department of Computer ScienceAalborg UniversityDenmark

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