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

Understand

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.