Detecting Communities in Networks by Merging Cliques
arXiv:1202.0480 · doi:10.1109/ICICISYS.2009.5358036
Abstract
Many algorithms have been proposed for detecting disjoint communities (relatively densely connected subgraphs) in networks. One popular technique is to optimize modularity, a measure of the quality of a partition in terms of the number of intracommunity and intercommunity edges. Greedy approximate algorithms for maximizing modularity can be very fast and effective. We propose a new algorithm that starts by detecting disjoint cliques and then merges these to optimize modularity. We show that this performs better than other similar algorithms in terms of both modularity and execution speed.
5 pages, 7 figures
References in corpus (6)
- 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
- Benchmark graphs for testing community detection algorithms
- CFinder: Locating cliques and overlapping modules in biological networks
- Extending the definition of modularity to directed graphs with overlapping communities