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I-Louvain: An Attributed Graph Clustering Method

David Combe 1 Christine Largeron 1 Mathias Géry 1 Elod Egyed-Zsigmond 2, * 
* Corresponding author
2 DRIM - Distribution, Recherche d'Information et Mobilité
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Modularity allows to estimate the quality of a partition into communities of a graph composed of highly interconnected vertices. In this article, we introduce a complementary measure, based on inertia, and specially conceived to evaluate the quality of a partition based on real attributes describing the vertices. We propose also I-Louvain, a graph nodes clustering method which uses our criterion , combined with Newman's modularity, in order to detect communities in attributed graph where real attributes are associated with the vertices. Our experiments show that combining the relational information with the attributes allows to detect the communities more efficiently than using only one type of information. In addition, our method is more robust to data degradation.
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Submitted on : Thursday, October 22, 2015 - 4:51:13 PM
Last modification on : Friday, September 30, 2022 - 11:34:16 AM
Long-term archiving on: : Saturday, January 23, 2016 - 4:03:16 PM


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David Combe, Christine Largeron, Mathias Géry, Elod Egyed-Zsigmond. I-Louvain: An Attributed Graph Clustering Method. Intelligent Data Analysis, LaHC, University of Saint-Etienne, France, Oct 2015, Saint-Etienne, France. ⟨10.1007/978-3-319-24465-5_16⟩. ⟨ujm-01219447⟩



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