Significance-Driven Graph Clustering

Authors:
Marco Gaertler;Robert Görke;Dorothea Wagner
Affiliations:
Faculty of Informatics, Universität Karlsruhe (TH),;Faculty of Informatics, Universität Karlsruhe (TH),;Faculty of Informatics, Universität Karlsruhe (TH),
Venue:
AAIM '07 Proceedings of the 3rd international conference on Algorithmic Aspects in Information and Management
Year:
2007

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Abstract

Modularity, the recently defined quality measure for clusterings, has attained instant popularity in the fields of social and natural sciences. We revisit the rationale behind the definition of modularity and explore the founding paradigm. This paradigm is based on the trade-off between the achieved quality and the expected quality of a clustering with respect to networks with similar intrinsic structure. We experimentally evaluate realizations of this paradigm systematically, including modularity, and describe efficient algorithms for their optimization. We confirm the feasibility of the resulting generality by a first systematic analysis of the behavior of these realizations on both artificial and on real-world data, arriving at remarkably good results of community detection.