论文标题
基准网络嵌入链接预测模型：我们是否正在取得进步？
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?
论文作者
论文摘要
网络嵌入方法将网络的节点映射到嵌入空间中的向量，以使这些表示形式有助于估计网络成对节点之间的相似性或接近性的一些概念。然后，通过下游预测任务的结果来展示这些节点表示的质量。但是，通常使用的基准任务（例如链接预测）当前复杂的评估管道和大量的设计选择。这与缺乏标准化的评估设置一起可能会掩盖该领域的实际进度。在本文中，我们旨在阐明网络嵌入方法的最新链接预测方法，并使用一致的评估管道显示，在过去几年中仅取得了很大的进步。我们在这里提出的新实施的基准，包括17种嵌入方法，还表明，即使简单的启发式方法，许多方法也表现出色。最后，我们认为标准化的评估工具可以修复这种情况并提高该领域的未来进步。
Network embedding methods map a network's nodes to vectors in an embedding space, in such a way that these representations are useful for estimating some notion of similarity or proximity between pairs of nodes in the network. The quality of these node representations is then showcased through results of downstream prediction tasks. Commonly used benchmark tasks such as link prediction, however, present complex evaluation pipelines and an abundance of design choices. This, together with a lack of standardized evaluation setups can obscure the real progress in the field. In this paper, we aim to shed light on the state-of-the-art of network embedding methods for link prediction and show, using a consistent evaluation pipeline, that only thin progress has been made over the last years. The newly conducted benchmark that we present here, including 17 embedding methods, also shows that many approaches are outperformed even by simple heuristics. Finally, we argue that standardized evaluation tools can repair this situation and boost future progress in this field.
