DHMConv: Directed Hypergraph Momentum Convolution Framework

Due to its capability to capture high-order information, the hypergraph model has shown greater potential than the graph model in various scenarios.Real-world entity relations frequently involve directionality, in order to express high-order information while capturing directional information in relationships, we present a directed hypergraph spatial convolution framework that is designed to acquire vertex embeddings of directed hypergraphs.The framework characterizes the information propagation of directed hypergraphs through two stages: hyperedge information aggregation and hyperedge information broadcasting.During the hyperedge information aggregation stage, we optimize the acquisition of hyperedge information using attention mechanisms.In the hyperedge information broadcasting stage, we leverage a directed hypergraph momentum encoder to capture the directional information of directed hyperedges.Experimental results on five publicly available directed graph datasets of three different categories demonstrate that our proposed DHMConv outperforms various commonly used graph and hypergraph models.