Abstract
The spreading of social networks in our society has aroused the interest of the scientific community in hard optimization problems related to them. Community detection is becoming one of the most challenging problems in social network analysis. The continuous growth of these networks makes exact methods for detecting communities not suitable for being used, since they require large computing times. In this paper, we propose a metaheuristic approach based on the Iterated Greedy methodology for detecting communities in large social networks. The computational results presented in this work show the relevance of the proposal when compared with traditional community detection algorithms in terms of both quality and computing time.
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