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.

翻译：二分网络是表示涉及两种不同类型实体之间交互系统的极佳模型，例如在线约会平台、求职服务或电子商务网站。这些模型可用于处理多项任务，其中链路预测是最有用的任务之一，尤其适用于设计推荐系统。然而，尽管该任务在单分（即标准）网络上已引起广泛关注，但在二分网络中的研究却远未达到同等程度。本研究通过实验比较19种能够处理二分图的链路预测方法，以弥补这一研究空白。其中部分方法直接来自文献，部分方法是我们从原本为单分网络设计的技术中改编而来。我们还提出将基于图卷积网络（GCN）的推荐系统重新定位为二分网络的新型链路预测解决方案。为开展实验，我们构建了包含三种不同拓扑结构的真实世界二分网络数据集的基准测试集。实验结果表明，近年来备受关注的基于GCN的个性化推荐系统能够在二分网络链路预测中取得良好效果。此外，不依赖任何学习过程的纯启发式度量方法（如结构扰动方法（SPM））同样能获得成功。