Communities detection in social network based on local edge centrality

Abstract Centrality measurement and community detection in complex social network are important in understanding network structures and analyzing network characteristics. In view of the importance of link strength weighting, a new centrality measurement of edge, called Local Edge Centrality (shortly, LEC), is proposed from a local perspective. Furthermore, we propose a new method for communities detection in social network, called Communities Detection based on LEC (shortly, CD-LEC), based on the idea of finding boundaries of community by the aid of centrality indices of edge LEC. The presented method utilizes the divisive method to obtain an initial partition of the network and then employs the modularity optimization to get the final partition of the network. To show the effectiveness of the proposed method, we empirically analyze this strategy on the real-world and artificial networks.

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