Dependent stotchastic blockmodels

A stochastic blockmodel is a generative model for blocks, where a block is a set of coherent nodes and relations between the nodes are explained by the corresponding pair of blocks. Most existing methods make use of both the presence and the absence of links between nodes, encoded by the adjacency matrix, to learn the corresponding models. In this paper, we present a new method in which we use only the presence of links to learn the model, exploiting the dependency between source and destination nodes, leading to a dependent stochastic blockmodel. We allow for mixed membership and the degrees of nodes in our dependent stochastic blockmodel. Experiments on the political books network and Twitter social network indicate that the behavior of our dependent stochastic blockmodel is superior to that of existing methods.