Return random walks for link prediction

In this paper we propose a new method, Return Random Walk, for link prediction to infer new intra-class edges while minimizing the amount of inter-class noise, and we show how to exploit it in an unsupervised densifier method, Dirichlet densification, which can be used to increase the edge density in undirected graphs, setting so that commute times can be better estimated by state-of-the-art methods. Moreover, this approach allows us to predict...