Learning with multi-resolution overlapping communities


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.

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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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Correspondence to Xufei Wang.

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Wang, X., Tang, L., Liu, H. et al. Learning with multi-resolution overlapping communities. Knowl Inf Syst 36, 517–535 (2013). https://doi.org/10.1007/s10115-012-0555-0

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