Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

Hypergraphs extend traditional graphs by allowing edges to connect multiple nodes, while
superhypergraphs further generalize this concept to represent even more complex …

Since their introduction, graph attention networks achieved outstanding results in graph
representation learning tasks. However, these networks consider only pairwise relations …

Graph neural networks (GNNs) have been used extensively as a backbone for social
recommendation. However, their application to a diverse range of situations is still rather …

Drug-Drug Interactions (DDIs) may hamper the functionalities of drugs, and in the worst
scenario, they may lead to adverse drug reactions (ADRs). Predicting all DDIs is a …

Abstract The assessment of Enterprise Credit Risk (ECR) is a critical technique for
investment decisions and financial regulation. Previous methods usually construct …

Predicting stock trends is conducive to optimize returns from stock investments, which gains
great interest from investors and researchers. Relations between stocks can provide …

3D object classification is an important task in computer vision. In order to explore the high-
order and multi-modal correlations among 3D data, we propose an adaptive multi …

Next Basket Recommendation (NBR) that recommends a basket of items to users has
become a promising promotion artifice for online businesses. The key challenge of NBR is …

Graph neural network (GNN) has gained increasing popularity in recent years owing to its
capability and flexibility in modeling complex graph structure data. Among all graph learning …