I-Louvain: An Attributed Graph Clustering Method

• Published in: Advances in Intelligent Data Analysis XIV Vol. 9385; pp. 181 - 192
• Main Authors: Combe, David, Largeron, Christine, Géry, Mathias, Egyed-Zsigmond, Előd
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2015; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Artificial intelligence; Attributed graph; Community detection; Graph clustering; Image processing; Modularity; Pattern recognition; Social network
• ISBN: 3319244647; 9783319244648
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-24465-5_16

Modularity allows to estimate the quality of a partition into communities of a graph composed of highly inter-connected 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.