PU matrix completion with graph information

Motivated by applications in recommendation systems and bioinformatics, we consider the problem of completing a low rank, partially observed binary matrix with graph information. We show that the corresponding problem can be set up in a positive and unlabeled data learning (referred to as PU learning in literature) framework. We make connections to convex optimization and show that existing greedy methods can be used to solve the problem. Experiments on simulated data as well as gene-disease associations data from bioinformatics show that using graphs, and adapting matrix completion in the PU learning setting, yield advantages over the standard binary matrix completion.