Scalable Method for Information Spread Control in Social Network

In this paper scalable and parallelized method for cluster analysis based on random walks is presented. The aim of the algorithm introduced in this paper is to detect dense sub graphs (clusters) and sparse sub graphs (bridges) 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 sub graphs by using algorithm provided in this work. Authors have also proposed new coefficient which measures quality of given clustering in a sense of an information spread control between clusters. Measure presented in this paper can be used for determining quality of whole partitioning or a single bridge.