Volume 45, Number 4, October-December 2011
|Page(s)||339 - 352|
|Published online||01 March 2012|
Bootstrap clustering for graph partitioning∗
IML – CNRS, 163
Av. de Luminy, 13009
Received: 14 June 2011
Accepted: 26 January 2012
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.
Mathematics Subject Classification: 05C85 / 90C35 / 62F40
Key words: Graph partitioning / clustering / modularity / consensus of partitions / bootstrap
© EDP Sciences, ROADEF, SMAI, 2012
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