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中的重要模型。尽管这些模型在简单图上的能力与局限性已被充分理解，但它们在知识图谱中的表现仍存在认知空白。本文旨在系统性地认识面向知识图谱链接预测任务的图神经网络全景。我们提出的分析为看似无关的模型提供了统一视角，并催生了一系列其他模型。通过对应的关系Weisfeiler-Leman算法，我们刻画了不同模型的表达能力。进一步的分析为图神经网络所捕获的函数类提供了精确的逻辑特征描述。本文的理论发现解释了实践中广泛采用的设计选择的优势，并通过实验验证加以佐证。