# Bootstrap clustering for graph partitioning∗

Philippe Gambette; Alain Guénoche

RAIRO - Operations Research (2012)

- Volume: 45, Issue: 4, page 339-352
- ISSN: 0399-0559

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topGambette, Philippe, and Guénoche, Alain. "Bootstrap clustering for graph partitioning∗." RAIRO - Operations Research 45.4 (2012): 339-352. <http://eudml.org/doc/222512>.

@article{Gambette2012,

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.},

author = {Gambette, Philippe, Guénoche, Alain},

journal = {RAIRO - Operations Research},

keywords = {Graph partitioning; clustering; modularity; consensus of partitions; bootstrap; graph partitioning},

language = {eng},

month = {3},

number = {4},

pages = {339-352},

publisher = {EDP Sciences},

title = {Bootstrap clustering for graph partitioning∗},

url = {http://eudml.org/doc/222512},

volume = {45},

year = {2012},

}

TY - JOUR

AU - Gambette, Philippe

AU - Guénoche, Alain

TI - Bootstrap clustering for graph partitioning∗

JO - RAIRO - Operations Research

DA - 2012/3//

PB - EDP Sciences

VL - 45

IS - 4

SP - 339

EP - 352

AB - 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.

LA - eng

KW - Graph partitioning; clustering; modularity; consensus of partitions; bootstrap; graph partitioning

UR - http://eudml.org/doc/222512

ER -

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