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Bootstrap clustering for graph partitioning∗

Published online by Cambridge University Press:  01 March 2012

Philippe Gambette  and
Alain Guénoche
Philippe Gambette
Affiliation:
IML – CNRS, 163 Av. de Luminy, 13009 Marseille, France. guenoche@iml.univ-mrs.fr
Alain Guénoche
Affiliation:
IML – CNRS, 163 Av. de Luminy, 13009 Marseille, France. guenoche@iml.univ-mrs.fr

Abstract

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Given a simple undirected weighted or unweighted graph, we try to cluster the vertex set into communities and also to quantify the robustness of these clusters. For that task, we propose a new method, called bootstrap clustering which consists in (i) defining a new clustering algorithm for graphs, (ii) building a set of graphs similar to the initial one, (iii) applying the clustering method to each of them, making a profile (set) of partitions, (iv) computing a consensus partition for this profile, which is the final graph partitioning. This allows to evaluate the robustness of a cluster as the average percentage of partitions in the profile joining its element pairs ; this notion can be extended to partitions. Doing so, the initial and consensus partitions can be compared. A simulation protocol, based on random graphs structured in communities is designed to evaluate the efficiency of the Bootstrap Clustering approach.

Keywords

Graph partitioningclusteringmodularityconsensus of partitionsbootstrap

Information

Type
Research Article
Information
RAIRO - Operations Research , Volume 45 , Issue 4 , October 2011 , pp. 339 - 352
Copyright
© EDP Sciences, ROADEF, SMAI, 2012

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