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Community Detection by Node Betweenness and Similarity in Complex Network
Author(s) -
Chong Feng,
Jianxu Ye,
Jianlu Hu,
Hui Yuan
Publication year - 2021
Publication title -
complexity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 61
eISSN - 1099-0526
pISSN - 1076-2787
DOI - 10.1155/2021/9986895
Subject(s) - betweenness centrality , complex network , computer science , node (physics) , similarity (geometry) , data mining , community structure , perspective (graphical) , artificial intelligence , series (stratigraphy) , algorithm , machine learning , mathematics , centrality , engineering , statistics , paleontology , structural engineering , world wide web , image (mathematics) , biology
Community detection of complex networks has always been a hot issue. With the mixed parameters μ increase in network complexity, community detection algorithms need to be improved. Based on previous work, the paper designs a novel algorithm from the perspective of node betweenness properties and gives the detailed steps of the algorithm and simulation results. We compare the proposed algorithm with a series of typical algorithms through experiments on synthetic and actual networks. Experimental results on artificial and real networks demonstrate the effectiveness and superiority of our algorithm.

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