@inproceedings{76910884b0404ef38c9b1b652462063d,
title = "Community detection using cooperative co-evolutionary differential evolution",
abstract = "In many scientific fields, from biology to sociology, community detection in complex networks has become increasingly important. This paper, for the first time, introduces Cooperative Co-evolution framework for detecting communities in complex networks. A Bias Grouping scheme is proposed to dynamically decompose a complex network into smaller subnetworks to handle large-scale networks. We adopt Differential Evolution (DE) to optimize network modularity to search for an optimal partition of a network. We also design a novel mutation operator specifically for community detection. The resulting algorithm, Cooperative Co-evolutionary DE based Community Detection (CCDECD) is evaluated on 5 small to large scale real-world social and biological networks. Experimental results show that CCDECD has very competitive performance compared with other state-of-the-art community detection algorithms. {\textcopyright} 2012 Springer-Verlag.",
keywords = "Differential Evolution, Community Detection, Collaboration Network, Normalize Mutual Information, Network Modularity",
author = "Qiang HUANG and Thomas WHITE and Guanbo JIA and Mirco MUSOLESI and Nil TURAN and Ke TANG and Shan HE and HEATH, \{John K.\} and Xin YAO",
year = "2012",
doi = "10.1007/978-3-642-32964-7\_24",
language = "English",
isbn = "9783642329630",
series = "Lecture Notes in Computer Science",
publisher = "Springer Berlin Heidelberg",
pages = "235--244",
editor = "COELLO, \{Carlos A. Coello\} and Vincenzo CUTELLO and Kalyanmoy DEB and Stephanie FORREST and Giuseppe NICOSIA and Mario PAVONE",
booktitle = "Parallel Problem Solving from Nature : PPSN XII : 12th International Conference, Taormina, Italy, September 1-5, 2012, Proceedings, Part II",
note = "12th International Conference on Parallel Problem Solving from Nature ; Conference date: 01-09-2012 Through 05-09-2012",
}