Community Detection in Complex Networks Using Genetic Algorithms
arXiv:0711.0491
Abstract
Community detection is an important research topic in complex networks. We present the employment of a genetic algorithm to detect communities in complex networks which is based on optimizing network modularity. It does not need any prior knowledge about the number of communities. Its performance is tested on two real life networks with known community structures and a set of synthetic networks. As the performance measure an information theoretical metric variation of information is used. The results are promising and in some cases better than previously reported studies.
Based on arXiv:cond-mat/0604419. 6 pages, 4 figures
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- Graph Neural Network Encoding for Community Detection in Attribute Networks
- A new measure of modularity density for community detection
- Quantitative Function and Algorithm for Community Detection in Bipartite Networks
- GA Based Q-Attack on Community Detection