Knowledge and Information Systems

, Volume 36, Issue 2, pp 517–535 | Cite as

Learning with multi-resolution overlapping communities

  • Xufei WangEmail author
  • Lei Tang
  • Huan Liu
  • Lei Wang
Short Paper


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.


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



We appreciate the authors of  [14] for sharing their source code for our empirical study. We thank the reviewers for their insightful comments. This work is, in part, sponsored by AFOSR and ONR.


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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Computer Science and EngineeringArizona State UniversityTempeUSA
  2. 2.Advertising SciencesWalmart LabsSanta ClaraUSA
  3. 3.School of Computer Science and Software EngineeringUniversity of WollongongWollongongAustralia

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