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Found 3 papers, 2 papers with code
Link Prediction with Relational Hypergraphs
Link prediction with knowledge graphs has been thoroughly studied in graph machine learning, leading to a rich landscape of graph neural network architectures with successful applications.
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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