Mode Hypergraph Neural Network.

Abstract

The hypergraph neural network (HGNN) is an emerging powerful tool for modeling and learning complex, high-order correlations among entities upon hypergraph structures. While existing HGNN-based approaches excel in modeling high-order correlations among data using hyperedges, they often have difficulties in distinguishing diverse semantics (e.g., bioactivities between drug and target in biological networks) of different correlations, making it challenging to learn accurate final representations. The underlying reason is that the specific semantic information of each hyperedge cannot be captured and distinguished during the modeling and learning process. To address this, we propose a mode HGNN ( $\textsf {MHGNN}$ ) framework that extends the vanilla hypergraph structure by endowing hyperedges with mode information for encapsulating their semantics and then performs mode-aware high-order message passing upon mode hypergraph for achieving comprehensive node representations. Extensive evaluations on four real-world datasets under two representative tasks have demonstrated the outstanding performance of $\textsf {MHGNN}$ against the state of the arts.