Hypergraph Neural Network Hawkes Process

Abstract

In real-world application, the temporal asynchronous event sequences are ubiquitous, such as social network, financial engineering, and medical diagonostics, and so on. These data usually show certain intrinsic high-order dependency characteristics. To this end, we propose a hypergraph neural network Hawkes process (HGHP) model, which can extract the high-order correlation from the data through the hypergraph neural network and encode dependent relationships into the hypergraph structure. When processing event sequence data, this method obtains the correlation matrix between different events through hyperedge convolution, and then obtains the latent representation for the event sequence based on the correlation between the data. We conduct experiments on multiple public datasets. Our proposed HGHP model achieves 86.6% accuracy on MIMIC-II dataset, 62.42% on Financial dataset, and 46.79% on Stackoverflow, which is outperforming existing baseline models.