Clustering attributed graphs: Models, measures and methods
By Cécile Bothorel, Juan David Cruz, Matteo Magnani, Barbora Micenková. 2015
244 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics
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In Artificial Intelligence
- Stephan Günnemann
- Aleksandar Bojchevski
- Aaron Clauset
- Mark Newman
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In Statistical and Nonlinear Physics
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- Jure Leskovec
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Citations in the corpus
- Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure (2018, background): “Clustering of attributed graphs – a special type of multiview data – has become an important research field (Bothorel et al. 2015), with application domains from social networks, over e-commerce, to gene analysis.”
- Structure and inference in annotated networks (2015, background): “Finally, combining equations (13)–(17) and substituting into equation (12), our complete expression for the log-likelihood, neglecting constants, is”
- Community detection in Attributed Network (2018, background): “Recently, only few recent studies have addressed the problem of clustering in attributed networks [24]; [4]; [21].”