Learning with multi-resolution overlapping communities

Xufei Wang, Lei Tang, Huan Liu, Lei Wang

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


A recent surge of participatory web and social media has created a new laboratory for studying human relations and collective behavior on an unprecedented scale. In this work, we study the predictive power of social connections to determine the preferences or behaviors of individuals such as whether a user supports a certain political view, whether one likes a product, whether she would like to vote for a presidential candidate, etc. Since an actor is likely to participate in multiple different communities with each regulating the actor's behavior in varying degrees, and a natural hierarchy might exist between these communities, we propose to zoom into a network at multiple different resolutions and determine which communities reflect a targeted behavior. We develop an efficient algorithm to extract a hierarchy of overlapping communities. Empirical results on social media networks demonstrate the promising potential of the proposed approach in real-world applications.

Original languageEnglish (US)
Pages (from-to)517-535
Number of pages19
JournalKnowledge and Information Systems
Issue number2
StatePublished - Aug 2013


  • Hierarchical clustering
  • Multi-resolution
  • Network-based classification
  • Overlapping communities
  • Social dimensions

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence

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