Link Prediction in Bipartite Networks

TL;DR

This paper compares 19 link prediction methods for bipartite networks, introduces GCN-based recommendation systems as a novel approach, and evaluates their effectiveness on real-world datasets.

Contribution

It provides the first comprehensive experimental comparison of link prediction methods on bipartite graphs and proposes GCN-based recommendation systems for this task.

Findings

GCN-based recommendation systems perform well in bipartite link prediction

Heuristic methods like Structural Perturbation Method are also effective

Experimental results are based on a benchmark of three real-world datasets

Abstract

Bipartite networks serve as highly suitable models to represent systems involving interactions between two distinct types of entities, such as online dating platforms, job search services, or ecommerce websites. These models can be leveraged to tackle a number of tasks, including link prediction among the most useful ones, especially to design recommendation systems. However, if this task has garnered much interest when conducted on unipartite (i.e. standard) networks, it is far from being the case for bipartite ones. In this study, we address this gap by performing an experimental comparison of 19 link prediction methods able to handle bipartite graphs. Some come directly from the literature, and some are adapted by us from techniques originally designed for unipartite networks. We also propose to repurpose recommendation systems based on graph convolutional networks (GCN) as a novel link prediction solution for bipartite networks. To conduct our experiments, we constitute a benchmark of 3 real-world bipartite network datasets with various topologies. Our results indicate that GCN-based personalized recommendation systems, which have received significant attention in recent years, can produce successful results for link prediction in bipartite networks. Furthermore, purely heuristic metrics that do not rely on any learning process, like the Structural Perturbation Method (SPM), can also achieve success.