  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.
