Hypergraphs as an expressive and general structure have attracted
considerable attention from various research domains. Most existing hypergraph
node representation learning techniques are based on graph neural networks, and
thus adopt the two-stage message passing paradigm (i.e. node -> hyperedge ->
node). This paradigm only focuses on local information propagation and does not
effectively take into account global information, resulting in less optimal
representations. Our theoretical analysis of representative two-stage message
passing methods shows that, mathematically, they model different ways of local
message passing through hyperedges, and can be unified into one-stage message
passing (i.e. node -> node). However, they still only model local information.
Motivated by this theoretical analysis, we propose a novel one-stage message
passing paradigm to model both global and local information propagation for
hypergraphs. We integrate this paradigm into HGraphormer, a Transformer-based
framework for hypergraph node representation learning. HGraphormer injects the
hypergraph structure information (local information) into Transformers (global
information) by combining the attention matrix and hypergraph Laplacian.
Extensive experiments demonstrate that HGraphormer outperforms recent
hypergraph learning methods on five representative benchmark datasets on the
semi-supervised hypernode classification task, setting new state-of-the-art
performance, with accuracy improvements between 2.52% and 6.70%. Our code and
datasets are available.

本研究提出了一种新颖的单阶段信息传递范式，用于超图中的全局和局部信息传播模型，在超图节点表示学习的 Transformer 框架 HGraphormer 中引入此范式，通过结合注意力矩阵和超图拉普拉斯矩阵将超图结构信息注入 Transformer，实验表明 HGraphormer 在半监督超节点分类任务上表现优于最新的超图学习方法，提升准确率在 2.52% 至 6.70% 之间。