Dynamic hypergraph neural networks based on key hyperedges

The technique of ynamic ypergraph Neural Networks based on ey yperedges (DHKH) model is proposed in this paper. Considering that the graph structure data in the real world is not uniformly distributed both semantically and structurally, we define the key hyperedge as the subgraph composed of a small number of key nodes and related edges in a graph. The key hyperedge can capture the key high-order structure information, which is able to enhance global topology expression. With the supporting of hyperedge and key hyperedge, can aggregate the high-order information and key information. In our experiments, DHKH shows good performance on multiple datasets, especially on the SZ dataset and LOS dataset which have inherently some key structures.