Ranking entities in heterogeneous multiple relation social networks using random walks

Abstract

A Social Network or Information Network is a structure made up of nodes representing entities, and edges representing the relationships among nodes. Understanding the behaviour of social networks is known as Social Network Analysis (SNA). One of the most important applications of SNA is to find the similarity/relevance among entities in the network for a specific query. Finding the relevance between different entities, we are able to rank them based on each other. Ranking a set of entities with respect to one instance is required in many application domains. For example, in E-Advertisement, the goal is to show the most related advertisement to each user. This essentially means to rank the advertisements based on each user and to show the high ranked ones to the user. In this study we focus on ranking the entities in heterogeneous multiple relation social networks, networks for which nodes belong to different classes and relationships have different types.