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Found 3 papers, 1 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.
Cooperative Graph Neural Networks
Graph neural networks are popular architectures for graph machine learning, based on iterative computation of node representations of an input graph through a series of invariant transformations.
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.
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