Affiliations: Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Correspondence:
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Corresponding author: Pegah Barekati, Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran. E-mail:[email protected]
Abstract: Social networks are generally sets of individuals or organizations that are
connected with one or more links. Usually, social networks are presented by
undirected graphs, where the set of vertices V and the set of edges E state
the individuals and relation between them respectively. One of the most
applicable problems in these networks is the centrality values allocation
problem to the vertices and edges. Recently, a new evaluation criterion for
the edge centrality so called centrality index of k paths has been proposed
which is based on intranet issuing the messages along with random paths
composed of k edges. From the other side, it has been vivid the importance
of computing the edges centrality through these years. In this study, by
referring to message propagation along random paths, a new diffusion model
was reached by applying heat diffusion algorithm. This model was based such
that the vertex on the way of heat diffusion of most of vertices could be
considered as an important node and it could obtain centrality edges by
scoring the edges on the heat diffusion path. The proposed technique was
compared with centrality drawing method by means of random paths of length $k$
and the results of analyzing the algorithm performance on online large
social networks' data set show a remarkable efficacy of the proposed method
to the mentioned method. Utilizing known large social networks in evaluation
proves the efficiency of the proposed method for analyzing the large scale
network.
Keywords: Social networks, edge centrality, betweenness centrality, heat diffusion
