Bipartite Edge Prediction via Transductive Learning over Product Graphs
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1880-1888, 2015.
This paper addresses the problem of predicting the missing edges of a bipartite graph where each side of the vertices has its own intrinsic structure. We propose a new optimization framework to map the two sides of the intrinsic structures onto the manifold structure of the edges via a graph product, and to reduce the original problem to vertex label propagation over the product graph. This framework enjoys flexible choices in the formulation of graph products, and supports a rich family of graph transduction schemes with scalable inference. Experiments on benchmark datasets for collaborative filtering, citation network analysis and prerequisite prediction of online courses show advantageous performance of the proposed approach over other state-of-the-art methods.