Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Natural communities in large linked networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Genetic clustering of social networks using random walks
Computational Statistics & Data Analysis
Detecting Overlapping Community Structures in Networks
World Wide Web
Agglomerative genetic algorithm for clustering in social networks
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Community detection in complex networks using collaborative evolutionary algorithms
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
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The detection of communities is an important problem, intensively investigated in recent years, to uncover the complex interconnections hidden in networks. In this paper a genetic based approach to discover communities in networks is proposed. The algorithm optimizes a simple but efficacious fitness function able to identify densely connected groups of nodes with sparse connections between groups. The method is efficient because the variation operators are modified to take into consideration only the actual correlations among the nodes, thus sensibly reducing the search space of possible solutions. Experiments on synthetic and real life networks show the ability of the method to successfully detect the network structure.