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• W2916684493 abstract "Community detection is one of the primary tools to discover useful information that is hidden in complex networks. Some community detection algorithms for bipartite networks have been proposed from various viewpoints. However, the performance of these algorithms deteriorates when the community structure becomes unclear. Enhancing community structure remains a nontrivial task. In this paper, we propose a community detection algorithm, called ECD, that enhances community structure in bipartite networks. In the proposed ECD, the topology of a network is modified by reducing unnecessary edges that are connected to neighboring low-weight communities. Therefore, an ambiguous community structure is converted into a structure that is much clearer than the original structure. The experimental results on both artificial and real-world networks verify the accuracy and reliability of our algorithm. Compared with existing community detection algorithms using state-of-the-art methods, our algorithm has better performance." @default.
• W2916684493 created "2019-03-02" @default.
• W2916684493 creator A5035318981 @default.
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• W2916684493 date "2019-03-10" @default.
• W2916684493 modified "2023-10-17" @default.
• W2916684493 title "Community detection by enhancing community structure in bipartite networks" @default.
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• W2916684493 doi "https://doi.org/10.1142/s0217984919500763" @default.
• W2916684493 hasPublicationYear "2019" @default.
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