Bootstrap clustering for graph partitioning∗

Philippe Gambette; Alain Guénoche

RAIRO - Operations Research (2012)

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

Abstract

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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.

How to cite

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Gambette, 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 -

References

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