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Bootstrap clustering for graph partitioning

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.
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Contributor : Philippe Gambette Connect in order to contact the contributor
Submitted on : Wednesday, March 7, 2012 - 12:57:39 AM
Last modification on : Thursday, September 29, 2022 - 2:21:15 PM
Long-term archiving on: : Friday, June 8, 2012 - 2:20:51 AM


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Philippe Gambette, Alain Guénoche. Bootstrap clustering for graph partitioning. RAIRO - Operations Research, 2011, 45 (4), pp.339-352. ⟨10.1051/ro/2012001⟩. ⟨hal-00676989⟩



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