Presentation of Fuzzy Model to Compute the Edge Betweenness Centrality in Social Networks

Abstract

Nowadays, we live in network area. The area through which the formation of various social network, new communicative and informing methods are introduced to the widespread social communications. A social network is a social structure which is made out of individuals and meanwhile, by the pass of time, the analyzing these social network will gain increasing primacy. In this research, one of the parameters of social network analysis called edge betweenness centrality is introduced. Edge betweenness is an edge to compute the shortest paths between pair of nodes in the network that passes through it most frequently. In this research, to detect the communities through edge betweenness centrality algorithm, a method is introduced in such a way that each edge by receiving one fuzzy membership degree in the interval [1, 0] the measure of its effect on the network will be different. One of the features of this algorithm that makes it distinguished from others is the application of fuzzy logic to detect the communities of social network. Then by introducing the density of each cluster the density measure of the communities graph is computed through considering the fuzzy detected structures. The finding of the implementation of algorithm indicated that introduced algorithm to compute the density of samples and to detect the number of mono-nodes while clustering has revealed more accuracy rather than the related works.