SwePub - search: WFRF:(Bothorel Cecile)

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1.	Bothorel, Cecile, et al. (author) Clustering attributed graphs : models, measures and methods 2015 In: Network Science. - : Cambridge University Press (CUP). - 2050-1242 .- 2050-1250. ; 3:3, s. 408-444 Journal article (peer-reviewed)abstract Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models as attributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing, and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.

Bothorel, Cecile, et al. (author)
Clustering attributed graphs : models, measures and methods
2015
In: Network Science. - : Cambridge University Press (CUP). - 2050-1242 .- 2050-1250. ; 3:3, s. 408-444
Journal article (peer-reviewed)abstract
- Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models as attributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing, and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.

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