On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Journal of Global Optimization
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
Recent advances in differential evolution: a survey and experimental analysis
Artificial Intelligence Review
Identifying and evaluating community structure in complex networks
Pattern Recognition Letters
Differential evolution in constrained numerical optimization: An empirical study
Information Sciences: an International Journal
Protein complex discovery from protein interaction network with high false-positive rate
EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters
IEEE Transactions on Evolutionary Computation
Community detection using cooperative co-evolutionary differential evolution
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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The community detection in complex networks is an important problem in many scientific fields, from biology to sociology. This paper proposes a new algorithm, Differential Evolution based Community Detection (DECD), which employs a novel optimization algorithm, differential evolution (DE) for detecting communities in complex networks. DE uses network modularity as the fitness function to search for an optimal partition of a network. Based on the standard DE crossover operator, we design a modified binomial crossover to effectively transmit some important information about the community structure in evolution. Moreover, a biased initialization process and a clean-up operation are employed in DECD to improve the quality of individuals in the population. One of the distinct merits of DECD is that, unlike many other community detection algorithms, DECD does not require any prior knowledge about the community structure, which is particularly useful for its application to real-world complex networks where prior knowledge is usually not available. We evaluate DECD on several artificial and real-world social and biological networks. Experimental results show that DECD has very competitive performance compared with other state-of-the-art community detection algorithms.