Detecting groups of similar components in complex networks
arXiv:0808.1612 · doi:10.1088/1367-2630/10/12/123023
Abstract
We study how to detect groups in a complex network each of which consists of component nodes sharing a similar connection pattern. Based on the mixture models and the exploratory analysis set up by Newman and Leicht (Newman and Leicht 2007 {\it Proc. Natl. Acad. Sci. USA} {\bf 104} 9564), we develop an algorithm that is applicable to a network with any degree distribution. The partition of a network suggested by this algorithm also applies to its complementary network. In general, groups of similar components are not necessarily identical with the communities in a community network; thus partitioning a network into groups of similar components provides additional information of the network structure. The proposed algorithm can also be used for community detection when the groups and the communities overlap. By introducing a tunable parameter that controls the involved effects of the heterogeneity, we can also investigate conveniently how the group structure can be coupled with the heterogeneity characteristics. In particular, an interesting example shows a group partition can evolve into a community partition in some situations when the involved heterogeneity effects are tuned. The extension of this algorithm to weighted networks is discussed as well.
14 pages, 10 figures, latex, more discussions added, typos cleared
References in corpus (27)
- Emergence of scaling in random networks
- Statistical mechanics of complex networks
- The structure and function of complex networks
- Community structure in social and biological networks
- Finding and evaluating community structure in networks
- Modularity and community structure in networks
- Finding community structure in networks using the eigenvectors of matrices
- Cooperative Game Theory Approaches for Network Partitioning
- Resolution limit in community detection
- Comparing community structure identification
- Community structure in directed networks
- Modularity from Fluctuations in Random Graphs and Complex Networks
- Synchronization reveals topological scales in complex networks
- An information-theoretic framework for resolving community structure in complex networks
- Mixture models and exploratory analysis in networks
- Epidemic spreading in correlated complex networks
- Size reduction of complex networks preserving modularity
- Distance, dissimilarity index, and network community structure
- Modularity and Extreme Edges of the Internet
- Network Landscape from a Brownian Particle's Perspective
- Spectral coarse-graining of complex networks
- Network synchronization of groups
- Cascading Dynamics in Modular Networks
- Synchronization in large directed networks of coupled phase oscillators
- Disease Spreading in Structured Scale-Free Networks
- Unveiling community structures in weighted networks
- Inversion method for content-based networks