Modeling and detecting community hierarchies

Authors:
Maria Florina Balcan;Yingyu Liang
Affiliations:
School of Computer Science, Georgia Institute of Technology, Atlanta, GA;School of Computer Science, Georgia Institute of Technology, Atlanta, GA
Venue:
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
Year:
2013

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Abstract

Community detection has in recent years emerged as an invaluable tool for describing and quantifying interactions in networks. In this paper we propose a theoretical model that explicitly formalizes both the tight connections within each community and the hierarchical nature of the communities. We further present an efficient algorithm that provably detects all the communities in our model. Experiments demonstrate that our definition successfully models real world communities, and our algorithm compares favorably with existing approaches.