VR-GNN: variational relation vector graph neural network for modeling homophily and heterophily

Abstract

Graph Neural Networks (GNNs) have achieved remarkable success in diverse real-world applications. Traditional GNNs are designed based on homophily, which leads to poor performance under heterophily scenarios. Most current solutions deal with heterophily mainly by modeling the heterophily edges as data noises or high-frequency signals, treating all heterophilic edges as being of the same semantic. Consequently, they ignore the rich semantic information of these edges in heterophily graphs. To overcome this critic problem, we propose a novel GNN model based on relation vector translation named as Variational Relation Vector Graph Neural Network (VR-GNN). VR-GNN models relation generation and graph aggregation into an end-to-end model based on a variational inference framework. To be specific, the encoder utilizes the structure, feature and label to generate a fine-grained relation vector for each edge, which aims to infer its implicit semantic information. The decoder incorporates the generated relation vectors into the message-passing framework for deriving better node representations. We conduct extensive experiments on eight real-world datasets with different homophily-heterophily properties to verify model effectiveness. Extensive experimental results show that VR-GNN gains consistent and significant improvements against existing strong GNN methods under heterophily and competitive performance under homophily.