Overlapping community detection using neighborhood ratio matrix
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Elsevier
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Abstract. Full text article available at https://doi.org/10.1016/j.physa.2014.11.039
The participation of a node in more than one community is a common phenomenon in complex networks. However most existing methods, fail to identify nodes with multiple community affiliation, correctly. In this paper, a unique method to define overlapping community in complex networks is proposed, using the overlapping neighborhood ratio to represent relations between nodes. Matrix factorization is then utilized to assign nodes into their corresponding community structures. Moreover, the proposed method demonstrates the use of Perron clusters to estimate the number of overlapping communities in a network. Experimental results in real and artificial networks show, with great accuracy, that the proposed method succeeds to recover most of the overlapping communities existing in the network.
The participation of a node in more than one community is a common phenomenon in complex networks. However most existing methods, fail to identify nodes with multiple community affiliation, correctly. In this paper, a unique method to define overlapping community in complex networks is proposed, using the overlapping neighborhood ratio to represent relations between nodes. Matrix factorization is then utilized to assign nodes into their corresponding community structures. Moreover, the proposed method demonstrates the use of Perron clusters to estimate the number of overlapping communities in a network. Experimental results in real and artificial networks show, with great accuracy, that the proposed method succeeds to recover most of the overlapping communities existing in the network.
Keywords
Complex networks, Nodes, Overlapping community, Perron clusters, Network, Neighboring ratio, Data mining, Overlapping neighborhood ratio, Neighborhood ratio matrix, Ratio matrix, Matrix factorization, Perron clusters