Link Prediction with Relational Hypergraphs
Xingyue Huang Miguel Romero Orth Pablo Barceló Michael M. Bronstein İsmail İlkan Ceylan
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. Nonetheless, it remains challenging
to transfer the success of these architectures to link prediction with
relational hypergraphs. The presence of relational hyperedges makes link
prediction a task between $k$ nodes for varying choices of $k$, which is
substantially harder than link prediction with knowledge graphs, where every
relation is binary ($k=2$). 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. Through extensive empirical analysis, we validate the power of the
proposed model architectures on various relational hypergraph benchmarks. The
resulting model architectures substantially outperform every baseline for
inductive link prediction, and lead to state-of-the-art results for
transductive link prediction. Our study therefore unlocks applications of graph
neural networks to fully relational structures.