Voronoi diagram-based geometric approach for social network analysis

Abstract

Social network analysis is aimed at analyzing relationships between social network users. Such analysis aims at finding community detection, that is, group of closest people in a network. Usually, graph clustering techniques are used to identify groups. Here, we propose a computational geometric approach to analyze social network. A Voronoi diagram-based clustering algorithm is employed over embedded dataset in the Euclidean vector space to identify groups. Structure-preserving embedding technique is used to embed the social network dataset and learns a low-rank kernel matrix by means of a semi-definite program with linear constraints that captures the connectivity structure of the input graph.