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Clustering attributed graphs: Models, measures and methods

Published online by Cambridge University Press:  18 March 2015

CECILE BOTHOREL ,
JUAN DAVID CRUZ ,
MATTEO MAGNANI  and
BARBORA MICENKOVÁ
CECILE BOTHOREL
Affiliation:
Department of Logics in Uses, Social Science and Information Science, UMR CNRS 3192 Lab-STICC, Télécom Bretagne, Institut Mines-Télécom, Brest, France (e-mail: [email protected], [email protected])
JUAN DAVID CRUZ
Affiliation:
Department of Logics in Uses, Social Science and Information Science, UMR CNRS 3192 Lab-STICC, Télécom Bretagne, Institut Mines-Télécom, Brest, France (e-mail: [email protected], [email protected])
MATTEO MAGNANI
Affiliation:
Computing Science Division, IT Department, Uppsala University, Uppsala, Sweden (e-mail: [email protected])
BARBORA MICENKOVÁ
Affiliation:
Data Intensive Systems, Department of Computer Science, Aarhus University, Aarhus, Denmark (e-mail: [email protected])
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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.

Keywords

attributed graphmultilayer networkclusteringcommunity detection
Type
Research Article
Information
Network Science , Volume 3 , Issue 3 , September 2015 , pp. 408 - 444
Copyright
Copyright © Cambridge University Press 2015 

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