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This paper proposes an organized generalization of Newman and Girvan'smodularity measure for graph clustering. Optimized via a deterministicannealing scheme, this measure produces topologically ordered graph clusteringsthat lead to faithful and readable graph representations based on clusteringinduced graphs. Topographic graph clustering provides an alternative to moreclassical solutions in which a standard graph clustering method is applied tobuild a simpler graph that is then represented with a graph layout algorithm. Acomparative study on four real world graphs ranging from 34 to 1 133 verticesshows the interest of the proposed approach with respect to classical solutionsand to self-organizing maps for graphs.

Optimizing an Organized Modularity Measure for Topographic Graph Clustering: a Deterministic Annealing Approach