Pairwise structural role mining for user categorization in information cascades

It is well known that many social networks follow the homophily principle, dictating that individuals tend to connect with similar peers. Past studies focused on non-topological properties, such as the age, gender, beliefs or educations. In this paper we focus precisely on the topology itself, exploring the possible existence of pairwise role dependency, that is, purely structural homophily. We show that while pairwise dependency is necessary for some structural roles, it may be misleading for others. We also present SR-Diffuse, a novel method for identifying the structural roles of nodes within a network. It is an iterative algorithm following an optimization model able to learn simultaneously from topological features and structural homophily, combining both aspects. For assessing our method, we applied it in a classification problem in information cascades, comparing its performance against several baseline methods. The experimental results with Flickr and Digg data show that SR-Diffuse can improve the quality of the discovered roles and can better represent the profile of the individuals, leading to a better prediction of social classes within information cascades.