Improving Hypergraph Attention and Hypergraph Convolution Networks

Abstract

Graph Neural Networks (GNNs) are models that use the structure of graphs to better exploit the bilateral relationship between neighboring nodes. Some problems, however, require that we consider a more general relationship which involve not only two nodes but rather a group of nodes. This is the approach adopted in Hypergraph Convolution and Hypergraph Attention Networks (HGAT) [1]. In this paper, we first propose to incorporate a weight matrix into these networks which, as our experimentations show, can improve the performance of the models in question. The other novelty in our work is the introduction of self-loops in the graphs which again leads to slight improvements in the accuracy of our architecture(named iHGAN).