Multiobjective immune algorithm with nondominated neighbor-based selection
Evolutionary Computation
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
A Multi-objective Genetic Algorithm for Community Detection in Networks
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
Community detection using a measure of global influence
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
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The study of complex networks has received an enormous amount of attention from the scientific community in recent years. In this paper, we propose a multi-objective approach, named NNIA-Net, to discover communities in networks by employing Non-dominated Neighbor Immune Algorithm (NNIA). Our algorithm optimizes two objectives to find communities in networks' groups of vertices within which connections are dense, but between which connections are sparser. The method can produce a series of solutions which represent various divisions to the networks at different hierarchical levels. The number of subdivisions is automatically determined by the non-dominated individuals resulting from our algorithm. We demonstrate that our algorithm is highly efficient at discovering quality community structure in both synthetic and real-world network data. What's more, a new initialization method is proposed to improve the traditional initialization method by about 30% in running time.