Community detection based on "clumpiness" matrix in complex networks
arXiv:1105.0324 · doi:10.1016/j.physa.2011.12.017
Abstract
The "clumpiness" matrix of a network is used to develop a method to identify its community structure. A "projection space" is constructed from the eigenvectors of the clumpiness matrix and a border line is defined using some kind of angular distance in this space. The community structure of the network is identified using this borderline and/or hierarchical clustering methods. The performance of our algorithm is tested on some computer-generated and real-world networks. The accuracy of the results is checked using normalized mutual information. The effect of community size heterogeneity on the accuracy of the method is also discussed.
18 pages and 13 figures
References in corpus (9)
- Community detection in graphs
- Uncovering the overlapping community structure of complex networks in nature and society
- Finding community structure in networks using the eigenvectors of matrices
- Cooperative Game Theory Approaches for Network Partitioning
- Maps of random walks on complex networks reveal community structure
- Benchmark graphs for testing community detection algorithms
- Comparing community structure identification
- Community detection algorithms: a comparative analysis
- An information-theoretic framework for resolving community structure in complex networks