Abstract
Community detection is a basic tool to analyze complex networks. However, there are many community detection methods for unipartite networks while just a few methods for bipartite networks (BNs). In this paper, we propose a memetic algorithm (MACD-BNs) to identify communities in BNs. We use MACD-BNs to optimize two extended measures, namely Baber modularity (QB) and modularity density (QD), on real-life and synthetic networks respectively so as to compare their performance. We conclude that QD are more effective than QB when the size of communities is heterogeneous while QB is more suitable to detect communities with similar size. Besides, we also make a comparison between MACD-BNs and other community detection method and the results show the effectiveness of MACD-BNs.
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CITATION STYLE
Wang, X., & Liu, J. (2017). A Memetic Algorithm for Community Detection in Bipartite Networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10637 LNCS, pp. 89–99). Springer Verlag. https://doi.org/10.1007/978-3-319-70093-9_10
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