Hypergraph Node Classification With Graph Neural Networks
Bohan Tang Zexi Liu Keyue Jiang Siheng Chen Xiaowen Dong
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.