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Detecting Overlapping Communities in Location-Based Social Networks

  • Zhu Wang
  • Daqing Zhang
  • Dingqi Yang
  • Zhiyong Yu
  • Xingshe Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7710)

Abstract

With the recent surge of location-based social networks (LBSNs, e.g., Foursquare, Facebook Places), huge amount of digital footprints about users’ locations, profiles as well as their online social connections become accessible to service providers. Different from social networks (e.g., Flickr, Facebook) which have explicit groups for users to subscribe or join, LBSNs usually have no explicit community structure. In order to capitalize on the large number of potential users, quality community detection approach is needed so as to enable applications such as direct marketing, group tracking, etc. The diversity of people’s interests and behaviors when using LBSNs suggests that their community structures overlap. In this paper, based on the user-venue check-in relationship and user/venue attributes, we come out with a novel multi-mode multi-attribute edge-centric co-clustering (M 2 Clustering) framework to discover the overlapping communities of LBSNs users. By employing inter-mode/intra-mode features, the proposed framework is able to group like-minded users from different social perspectives. The efficacy of our approach is validated by intensive empirical evaluations using the collected Foursquare dataset of 266,838 users with 9,803,764 check-ins over 2,477,122 venues worldwide.

Keywords

Community Detection Overlapping Community Edge- Clustering Location-Based Social Networks 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhu Wang
    • 1
  • Daqing Zhang
    • 2
  • Dingqi Yang
    • 2
  • Zhiyong Yu
    • 2
  • Xingshe Zhou
    • 1
  1. 1.Northwestern Polytechnical UniversityXi’anChina
  2. 2.Institut TELECOM SudParisEvryFrance

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