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Found 3 papers, 2 papers with code
Link Prediction with Relational Hypergraphs
In this paper, we propose two frameworks for link prediction with relational hypergraphs and conduct a thorough analysis of the expressive power of the resulting model architectures via corresponding relational Weisfeiler-Leman algorithms, and also via some natural logical formalisms.
A Theory of Link Prediction via Relational Weisfeiler-Leman on 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.
Weisfeiler and Leman Go Relational
Namely, we investigate the limitations in the expressive power of the well-known Relational GCN and Compositional GCN architectures and shed some light on their practical learning performance.
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