Hypergraph Node Representation Learning with One-Stage Message Passing
Shilin Qu Weiqing Wang Yuan-Fang Li Xin Zhou Fajie Yuan
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.