Name
Abstract | ||
|---|---|---|
Fundamental to the identification of the architecture and organization of complex systems is the detection of modules, also called communities or clusters, through the use of graph partition methods. In this paper, we extend one of the most popular graph partition methods, modularity, to jointly preserve the structure of multiple networks using the multi-view technique. Under the assumption that the same modular structure is shared by all network realizations, we show that the multi-view approach is robust against scaling, noise and outliers. In addition, it can overcome some resolution limitations of the traditional modularity-based method. We demonstrate the performance of the combined modularity-multiview method in simulations and experimental data from a 191-subject functional brain network. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ACSSC.2013.6810435 | 2013 ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS |
Keywords | Field | DocType |
Modularity, Graph Partitioning, Multi-view Clustering, Complex Networks, Brain Networks | Complex system,Data mining,Modularity (networks),Computer science,Outlier,Theoretical computer science,Robustness (computer science),Mutual information,Graph partition,Cluster analysis,Modularity | Conference |
ISSN | Citations | PageRank |
1058-6393 | 0 | 0.34 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Teng Chang | 1 | 16 | 5.17 |
| Dimitrios Pantazis | 2 | 464 | 38.97 |