Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic clustering of social networks using random walks
Computational Statistics & Data Analysis
IEEE Transactions on Knowledge and Data Engineering
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
A novel similarity-based modularity function for graph partitioning
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Analyzing Voting Behavior in Italian Parliament: Group Cohesion and Evolution
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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The discovery of modular communities to uncover the complex interconnections hidden in networks is an intensively investigated problem in recent years. Many approaches optimize a quality function, modularity, that is also a validation measure of a network partition in clusters. The paper proposes an approach, based on Genetic Algorithms, that reveals community structure in networks by optimizing modularity. The method boosts the modularity of the partition obtained by the genetic algorithm by performing a local greedy search step on this partition. Experiments on synthetic and real life networks show that the method is able to successfully reveal highly modular network structure.