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• W2770533236 abstract "Community detection in networks has become a very important axis of research for understanding the structure of networks. Several methods have been proposed to detect the most optimal community structure in networks. In this article, we present a novel method for detecting community structure ComDBNS (Community Detection Based on Node Similarity) for unweighted and undirected networks; it performs in two steps. The first step uses the similarity between endpoints of each link to find the inter-community links to remove in order to create basic groups of nodes properly connected. In the second step we propose a strategy to merge these initial groups to identify the final community structure (with k communities or the structure that maximizes the modularity in Community Detection Based on Node Similarity and Modularity Q (ComDBNSQ)). The proposed method is tested on the real and computer-generated networks, and it demonstrates the effectiveness and correctness of the method. Also, the method saves the time complexity." @default.
• W2770533236 created "2017-12-04" @default.
• W2770533236 creator A5000194284 @default.
• W2770533236 creator A5081019870 @default.
• W2770533236 date "2018-02-01" @default.
• W2770533236 modified "2023-10-01" @default.
• W2770533236 title "Node similarity and modularity for finding communities in networks" @default.
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• W2770533236 doi "https://doi.org/10.1016/j.physa.2017.11.110" @default.
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