Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24465-5_16 | ADVANCES IN INTELLIGENT DATA ANALYSIS XIV |
Keywords | Field | DocType |
Attributed graph,Graph clustering,Social network,Community detection,Modularity | Data degradation,Social network,Pattern recognition,Correlation clustering,Vertex (geometry),Computer science,Theoretical computer science,Artificial intelligence,Clustering coefficient,Cluster analysis,Partition (number theory),Modularity | Conference |
Volume | ISSN | Citations |
9385 | 0302-9743 | 3 |
PageRank | References | Authors |
0.41 | 22 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Combe | 1 | 32 | 2.70 |
| Christine Largeron | 2 | 148 | 30.40 |
| Mathias Géry | 3 | 137 | 37.23 |
| Elöd Egyed-zsigmond | 4 | 87 | 16.64 |