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.

通过关系超图进行链接预测的研究，提出了两种框架，并通过相应的关系 Weisfeiler-Leman 算法和一些自然逻辑形式主义的广泛实证分析，证明了所提模型架构的表达能力。在各种关系超图基准测试中，所得到的模型架构明显优于每个基线模型，对于归纳链接预测具有领先水平的结果，从而开启了图神经网络在完全关系结构中的应用。