A parameterized probabilistic model of network evolution for supervised link prediction

Abstract

We introduce a new approach to the problem of link prediction for network structured domains, such as the Web, social networks, and biological networks. Our approach is based on the topological features of network structures, not on the node features. We present a novel parameterized probabilistic model of network evolution and derive an efficient incremental learning algorithm for such models, which is then used to predict links among the nodes. We show some promising experimental results using biological network data sets.