I-Louvain: An Attributed Graph Clustering Method

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 describin...

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