An approximation of betweenness centrality for Social Networks

Abstract

A challenge in the research of Social Networks is the large scale analysis of graphs. One of the most valuable metrics in the evaluation of graphs is betweenness-centrality. In this paper, we define an approximation of betweenness-centrality for the purpose of building a predictive model of Social Networks. The methodology presented describes a bounded distance approximation of betweenness-centrality designed for implementation within a parallel architecture. Through our proposed design pattern, we are able to leverage Big Data technologies to determine metrics in the context of ever expanding internet-based data resources.