Bipartite Edge Prediction via Transductive Learning over Product Graphs
Hanxiao Liu, Yiming Yang · 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 struc-ture. 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 prob-lem 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 net-work analysis and prerequisite prediction of on-line courses show advantageous performance of the proposed approach over other state-of-the-art methods. 1.