Clique Graphs and Overlapping Communities
arXiv:1009.0638 · doi:10.1088/1742-5468/2010/12/P12037
Abstract
It is shown how to construct a clique graph in which properties of cliques of a fixed order in a given graph are represented by vertices in a weighted graph. Various definitions and motivations for these weights are given. The detection of communities or clusters is used to illustrate how a clique graph may be exploited. In particular a benchmark network is shown where clique graphs find the overlapping communities accurately while vertex partition methods fail.
23 pages plus 16 additional pages in appendices
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Cited by in corpus (26)
- Community detection in networks: A user guide
- Overlapping Community Detection in Networks: the State of the Art and Comparative Study
- The ground truth about metadata and community detection in networks
- Parsimonious module inference in large networks
- Bayesian stochastic blockmodeling
- Map equation for link community
- Local dominance unveils clusters in networks
- Markov random walk under constraint for discovering overlapping communities in complex networks
- Universality of Performance Indicators based on Citation and Reference Counts
- Post-processing partitions to identify domains of modularity optimization
- Network structure, metadata and the prediction of missing nodes and annotations
- Network community detection using modularity density measures
- Adaptive Modularity Maximization via Edge Weighting Scheme
- Cycle Analysis of Directed Acyclic Graphs
- Local community extraction in directed networks
- Higher-order temporal network effects through triplet evolution
- Self-falsifiable Hierarchical Detection of Overlapping Communities On Social Networks
- Centralities in complex networks
- Exploring triad-rich substructures by graph-theoretic characterizations in complex networks
- On the Termination of Some Biclique Operators on Multipartite Graphs
- Multilayer Modularity Belief Propagation To Assess Detectability Of Community Structure
- Sequential locality of graphs and its hypothesis testing
- Hybrid evolving clique-networks and their communicability
- Improving community detection via community association strength scores
- Systematic assessment of the quality of fit of the stochastic block model for empirical networks
- Finding community structure using the ordered random graph model