Generalize to Fully Unseen Graphs: Learn Transferable Hyper-Relation Structures for Inductive Link Prediction

Inductive link prediction aims to infer missing triples on unseen graphs, which contain unseen entities and relations during training. The performances of existing inductive inference methods were hindered by the limited generalization capability in fully unseen graphs, which is rooted in the neglect of the intrinsic graph structure. In this paper, we aim to enhance the model's generalization ability to unseen graphs and thus propose a novel Hyper-Relation aware multi-views model (HyRel) for learning the global transferable structure of graphs. Distinct from existing studies, we introduce a novel perspective focused on learning the inherent hyper-relation structure consisting of the relation positions and affinity. The hyper-relation structure is independent of specific entities, relations, or features, thus allowing for transferring the learned knowledge to any unseen graphs. We adopt a multi-view approach to model the hyper-relation structure. HyRel incorporates neighborhood learning on each view, capturing nuanced semantics of relative relation position. Meanwhile, dual views contrastive constraints are designed to enforce the robustness of transferable structural knowledge. To the best of our knowledge, our work makes one of the first attempts to generalize the learning of hyper-relation structures, offering high flexibility and ease of use without reliance on any external resources. HyRel demonstrates SOTA performance compared to existing methods under extensive inductive settings, particularly on fully unseen graphs, and validates the efficacy of learning hyper-relation structures for improving generalization. The code is available online at https://github.com/hncps6/HyRel.