N-ary relational facts represent semantic correlations among more than two entities. While recent studies have developed link prediction (LP) methods to infer missing relations for knowledge graphs (KGs) containing n-ary relational facts, they are generally limited to transductive settings. Fully inductive settings, where predictions are made on previously unseen entities, remain a significant challenge. As existing methods are mainly entity embedding-based, they struggle to capture entity-independent logical rules. To fill in this gap, we propose an n-ary subgraph reasoning framework for fully inductive link prediction (ILP) on n-ary relational facts. This framework reasons over local subgraphs and has a strong inductive inference ability to capture n-ary patterns. Specifically, we introduce a novel graph structure, the n-ary semantic hypergraph, to facilitate subgraph extraction. Moreover, we develop a subgraph aggregating network, NS-HART, to effectively mine complex semantic correlations within subgraphs. Theoretically, we provide a thorough analysis from the score function optimization perspective to shed light on NS-HART's effectiveness for n-ary ILP tasks. Empirically, we conduct extensive experiments on a series of inductive benchmarks, including transfer reasoning (with and without entity features) and pairwise subgraph reasoning. The results highlight the superiority of the n-ary subgraph reasoning framework and the exceptional inductive ability of NS-HART.

Link prediction is a core task in graph machine learning with wide applications. However, little attention has been paid to link prediction between two group entities. This limits the application of the current approaches to many real-life problems, such as predicting collaborations between academic groups or recommending bundles of items to group users. Moreover, groups are often ephemeral or emergent, forcing the predicting model to deal with challenging inductive scenes. To fill this gap, we develop a framework composed of a GNN-based encoder and neural-based aggregating networks, namely the Mutual Multi-view Attention Networks (MMAN). First, we adopt GNN-based encoders to model multiple interactions among members and groups through propagating. Then, we develop MMAN to aggregate members' node representations into multi-view group representations and compute the final results by pooling pairwise scores between views. Specifically, several view-guided attention modules are adopted when learning multi-view group representations, thus capturing diversified member weights and multifaceted group characteristics. In this way, MMAN can further mimic the mutual and multiple interactions between groups. We conduct experiments on three datasets, including two academic group link prediction datasets and one bundle-to-group recommendation dataset. The results demonstrate that the proposed approach can achieve superior performance on both tasks compared with plain GNN-based methods and other aggregating methods.