Finding Betweenness in Dense Unweighted Graphs

Abstract—Social network analysis (SNA) aims to identify and better determine the relationship amongst data in a graph representation.The interpretation of several core SNA measures, degree, closeness and betweenness centrality of a node, have been the subject of extensive research in recent years. We concentrate on the betweenness property, which seeks to determine the relatedness of more than 2 nodes. We propose our betweenness in unweighted graph algorithm and compare it to the k-path centrality algorithm on two image collections. By design, our proposed algorithm is less restrictive with the ability to consider any subset of nodes for betweenness. Our findings also show our proposed algorithm has a much shorter execution time as compared to the k-path centrality algorithm. Keywords-social network analysis; betweenness centrality; so-cial networking graph model. I