Social Relation Inference via Label Propagation.

Collaboration networks are a ubiquitous way to characterize the interactions between people. In this paper, we consider the problem of inferring social relations in collaboration networks, such as the fields that researchers collaborate in, or the categories of projects that Github users work on together. Social relation inference can be formalized as a multi-label classification problem on graph edges, but many popular algorithms for semi-supervised learning on graphs only operate on the nodes of a graph. To bridge this gap, we propose a principled method which leverages the natural homophily present in collaboration networks. First, observing that the fields of collaboration for two people are usually at the intersection of their interests, we transform an edge labeling into node labels. Second, we use a label propagation algorithm to propagate node labels in the entire graph. Once the label distribution for all nodes has been obtained, we can easily infer the label distribution for all edges. Experiments on two large-scale collaboration networks demonstrate that our method outperforms the state-of-the-art methods for social relation inference by a large margin, in addition to running several orders of magnitude faster.