Identifying Bridges for Information Spread Control in Social Networks

Abstract

In this paper scalable method for cluster analysis based on random walks is presented. The main aim of the algorithm introduced in this paper is to detect dense subgraphs. Provided method has additional feature. It identifies groups of vertices which are responsible for information spreading among found clusters. The algorithm is sensitive to vertices assignment uncertainty. It distinguishes groups of nodes which form sparse clusters. These groups are mostly located in places crucial for information spreading so one can control signal propagation between separated dense subgraphs by using algorithm provided in this work.