Optimizing an organized modularity measure for topographic graph clustering: A deterministic annealing approach

Authors:
Fabrice Rossi;Nathalie Villa-Vialaneix
Affiliations:
BILab, Télécom ParisTech, LTCI - UMR CNRS 5141, France;Institut de Mathématiques de Toulouse, Université de Toulouse, France and IUT STID (Carcassonne), Université de Perpignan Via Domitia, France
Venue:
Neurocomputing
Year:
2010

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Abstract

This paper proposes an organized generalization of Newman and Girvan's modularity measure for graph clustering. Optimized via a deterministic annealing scheme, this measure produces topologically ordered graph clusterings that lead to faithful and readable graph representations based on clustering induced graphs. Topographic graph clustering provides an alternative to more classical solutions in which a standard graph clustering method is applied to build a simpler graph that is then represented with a graph layout algorithm. A comparative study on four real world graphs ranging from 34 to 1133 vertices shows the interest of the proposed approach with respect to classical solutions and to self-organizing maps for graphs.