Hypergraph modeling and hypergraph multi-view attention neural network for link prediction

Hypergraph neural networks are widely used in link prediction because of their ability to learn the high-order structure relationship. However, most existing hypergraph modeling relies on the attribute information of nodes. And as for the link prediction, missing links are not utilized when training link predictors, so conventional transductive hypergraph learning are generally not consistent with link prediction tasks. To address these limitations, we propose the Network Structure Linear Representation (NSLR) method to model hypergraph for general networks without node attribute information and the inductive hypergraph learning method Hypergraph Multi-view Attention Neural Network (HMANN) that learns the rich high-order structure information from node-level and hyperedge-level. Also, this paper put forwards a novel NSLR-HMANN link prediction algorithm based on NSLR and HMANN methods. Extensive comparison and ablation experiments show that the NSLR-HMANN link prediction algorithm achieves state-of-the-art performance on link prediction and has better performance on robustness. •The hypergraph modeling method without node information is proposed.•An inductive hypergraph learning method to explore the high-order structure.•We propose a link prediction algorithm using high-order structure information.•Experiments are conducted to verify the advanced performance of the algorithm.