Bootstrap clustering for graph partitioning
RAIRO - Operations Research - Recherche Opérationnelle, Tome 45 (2011) no. 4, pp. 339-352.

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.

DOI : 10.1051/ro/2012001
Classification : 05C85, 90C35, 62F40
Mots-clés : graph partitioning, clustering, modularity, consensus of partitions, bootstrap 
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     title = {Bootstrap clustering for graph partitioning},
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     pages = {339--352},
     publisher = {EDP-Sciences},
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Gambette, Philippe; Guénoche, Alain. Bootstrap clustering for graph partitioning. RAIRO - Operations Research - Recherche Opérationnelle, Tome 45 (2011) no. 4, pp. 339-352. doi : 10.1051/ro/2012001. http://www.numdam.org/articles/10.1051/ro/2012001/

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