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EgoClustering: Overlapping Community Detection via Merged Friendship-Groups

  • Bradley S. ReesEmail author
  • Keith B. Gallagher
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN, volume 6)

Abstract

There has been considerable interest in identifying communities within large collections of social networking data. Existing algorithms will classify an actor (node) into a single group, ignoring the fact that in real-world situations people tend to belong concurrently to multiple (overlapping) groups. Our work focuses on the ability to find overlapping communities. We use egonets to form friendship-groups. A friendship-group is a localized community as seen from an individual’s perspective that allows an actor to belong to multiple communities. Our algorithm finds overlapping communities and identifies key members that bind communities together. Additionally, we will highlight the parallel feature of the algorithm as a means of improving runtime performance, and the ability of the algorithm to run within a database and not be constrained by system memory.

Keywords

Betweenness Centrality Community Detection Sparse Graph Friendship Group Runtime Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors are grateful to Graham Cruickshank for his proofreading skill.

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Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceFlorida Institute of TechnologyMelbourneUSA

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