Graph neural networks are prominent models for representation learning over graph-structured data. While the capabilities and limitations of these models are well-understood for simple graphs, our understanding remains incomplete in the context of knowledge graphs. Our goal is to provide a systematic understanding of the landscape of graph neural networks for knowledge graphs pertaining to the prominent task of link prediction. Our analysis entails a unifying perspective on seemingly unrelated models and unlocks a series of other models. The expressive power of various models is characterized via a corresponding relational Weisfeiler-Leman algorithm. This analysis is extended to provide a precise logical characterization of the class of functions captured by a class of graph neural networks. The theoretical findings presented in this paper explain the benefits of some widely employed practical design choices, which are validated empirically.

翻译：图神经网络是图结构数据表示学习的重要模型。虽然这些模型在简单图上的能力与局限性已被充分理解，但在知识图谱背景下，我们的理解仍不完整。本文旨在系统理解知识图谱上针对链接预测这一重要任务的图神经网络格局。我们的分析为看似无关的模型提供了统一视角，并衍生出一系列其他模型。通过对应的关系型Weisfeiler-Leman算法，我们刻画了各类模型的表达能力。该分析进一步扩展，为图神经网络所捕获的函数类提供了精确的逻辑刻画。本文的理论发现解释了若干广泛采用的实践设计选择的优势，并通过实验验证了这些结论。