A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs

• URL: http://arxiv.org/abs/2302.02209v4
• Date: Thu, 26 Oct 2023 14:44:27 GMT
• Title: A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs
• Authors: Xingyue Huang, Miguel Romero Orth, \.Ismail \.Ilkan Ceylan, Pablo Barcel\'o
• Abstract summary: Graph neural networks are prominent models for representation learning over graph-structured data. Our goal is to provide a systematic understanding of the landscape of graph neural networks for knowledge graphs.
• Score: 6.379544211152605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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