Swarm intelligence
Linked
Discovering communities in complex networks
Proceedings of the 44th annual Southeast regional conference
GA-Net: A Genetic Algorithm for Community Detection in Social Networks
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Community detection in complex networks using collaborative evolutionary algorithms
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
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A collaborative evolutionary model is proposed to address the community structure detection problem in complex networks. The discovery of commmunities or organization of nodes in clusters (with dense intra-connections and comparatively sparse inter-cluster connections) is a hard problem of great importance in sociology, biology and computer science. Based on a natural problem-specific chromosome representation and fitness function, the proposed evolutionary model relies on collaborative selection and best-worst recombination to guide the search process efficiently towards promising solutions. The collaborative operators take into account information about an individual line best ancestor, global and worst individuals produced up to the current generation. The algorithm is able to detect non-overlapping communities in complex networks without the need to a-priori know the expected number of clusters. Computational experiments on several real-world social networks emphasize a good performance of the proposed algorithm compared to state-of-the-art models.