Learning representative nodes in social networks

Abstract

We study the problem of identifying representative users in social networks from an information spreading perspective. While traditional network measures such as node degree and PageRank have been shown to work well for selecting seed users, the resulting nodes often have high neighbour overlap and thus are not optimal in terms of maximising spreading coverage. In this paper we extend a recently proposed statistical learning approach called skeleton learning (SKE) to graph datasets. The idea is to associate each node with a random representative node through Bayesian inference. By doing so, a prior distribution defined over the graph nodes emerges where representatives with high probabilities lie in key positions and are mutually exclusive, reducing neighbour overlap. Evaluation with information diffusion experiments on real scientific collaboration networks shows that seeds selected using SKE are more effective spreaders compared with those selected with traditional ranking algorithms and a state-of-the-art degree discount heuristic. © Springer-Verlag 2013.