Discriminative Link Prediction using Local, Community, and Global Signals

Predicting plausible links that may emerge between pairs of nodes is an important task in social network analysis, with over a decade of active research. Here, we propose a novel framework for link prediction. It integrates signals from node features, the existing local link neighborhood of a node pair, community-level link density, and global graph properties. Our framework uses a stacked two-level learning paradigm. At the lower level, the first two kinds of features are processed by a novel local learner. Its outputs are then integrated with the last two kinds of features by a conventional discriminative learner at the upper-level. We also propose a new stratified sampling scheme for evaluating link prediction algorithms in the face of an extremely large number of potential edges, out of which very few will ever materialize. It is not tied to a specific application of link prediction, but robust to a range of application requirements. We report on extensive experiments with seven benchmark datasets and over five competitive baseline systems. The system we present consistently shows at least 10 percent accuracy improvement over state-of-the-art, and over 30 percent improvement in some cases. We also demonstrate, through ablation, that our features are complementary in terms of the signals and accuracy benefits they provide.