LEGER, Jean-Benoist; VACHER, Corinne; DAUDIN, Jean-Jacques

doi:10.1007/s11222-013-9395-3

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LEGER, Jean-Benoist
Mathématiques et Informatique Appliquées [MIA-Paris]

VACHER, Corinne
Biodiversité, Gènes & Communautés [BioGeCo]

DAUDIN, Jean-Jacques
Mathématiques et Informatique Appliquées [MIA-Paris]

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Statistics and Computing. 2014, vol. online first, n° 5, p. 18 p.

Springer Verlag (Germany)

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The analysis of complex networks is a rapidly growing topic with many applications in different domains. The analysis of large graphs is often made via unsupervised classification of vertices of the graph. Community detection is the main way to divide a large graph into smaller ones that can be studied separately. However another definition of a cluster is possible, which is based on the structural distance between vertices. This definition includes the case of community clusters but is more general in the sense that two vertices may be in the same group even if they are not connected. Methods for detecting communities in undirected graphs have been recently reviewed by Fortunato. In this paper we expand Fortunato's work and make a review of methods and algorithms for detecting essentially structurally homogeneous subsets of vertices in binary or weighted and directed and undirected graphs.< Réduire

Mots clés en anglais

graphs

clusters

random walk

spectral

clustering

stochastic

block

model

bipartite graphs

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Detection of structurally homogeneous subsets in graphs

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