Hypergraph Node Classification With Graph Neural Networks

Hypergraphs, with hyperedges connecting more than two nodes, are key for modelling higher-order interactions in real-world data. The success of graph neural networks (GNNs) reveals the capability of neural networks to process data with pairwise interactions. This inspires the usage of neural networks for data with higher-order interactions, thereby leading to the development of hypergraph neural networks (HyperGNNs). GNNs and HyperGNNs are typically considered distinct since they are designed for data on different geometric topologies. However, in this paper, we theoretically demonstrate that, in the context of node classification, most HyperGNNs can be approximated using a GNN with a weighted clique expansion of the hypergraph. This leads to WCE-GNN, a simple and efficient framework comprising a GNN and a weighted clique expansion (WCE), for hypergraph node classification. Experiments on nine real-world hypergraph node classification benchmarks showcase that WCE-GNN demonstrates not only higher classification accuracy compared to state-of-the-art HyperGNNs, but also superior memory and runtime efficiency.

Hypergraph Transformer for Semi-Supervised Classification

Hypergraphs play a pivotal role in the modelling of data featuring higher-order relations involving more than two entities. Hypergraph neural networks emerge as a powerful tool for processing hypergraph-structured data, delivering remarkable performance across various tasks, e.g., hypergraph node classification. However, these models struggle to capture global structural information due to their reliance on local message passing. To address this challenge, we propose a novel hypergraph learning framework, HyperGraph Transformer (HyperGT). HyperGT uses a Transformer-based neural network architecture to effectively consider global correlations among all nodes and hyperedges. To incorporate local structural information, HyperGT has two distinct designs: i) a positional encoding based on the hypergraph incidence matrix, offering valuable insights into node-node and hyperedge-hyperedge interactions; and ii) a hypergraph structure regularization in the loss function, capturing connectivities between nodes and hyperedges. Through these designs, HyperGT achieves comprehensive hypergraph representation learning by effectively incorporating global interactions while preserving local connectivity patterns. Extensive experiments conducted on real-world hypergraph node classification tasks showcase that HyperGT consistently outperforms existing methods, establishing new state-of-the-art benchmarks. Ablation studies affirm the effectiveness of the individual designs of our model.