Abstract
Recently, community detection in complex networks has attracted more and more attentions. However, real networks usually have number of overlapping communities. Many overlapping community detection algorithms have been developed. These methods usually consider the overlapping community detection as a single-objective optimization problem. This paper regards it as a multi-objective optimization problem and proposes a Multi-Objective evolutionary algorithm for Overlapping Community Detection (MOOCD). This algorithm simultaneously optimize two objective functions to get proper community partitions. Experiments on artificial and real networks illustrate the effectiveness of MOOCD.
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Du, J., Lai, J., Shi, C. (2013). Multi-Objective Optimization for Overlapping Community Detection. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53917-6_44
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DOI: https://doi.org/10.1007/978-3-642-53917-6_44
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