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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. [email protected]
Alain Guénoche
Affiliation:
IML – CNRS, 163 Av. de Luminy, 13009 Marseille, France. [email protected]
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Abstract

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
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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