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. Current solutions
deal with heterophily mainly by mixing high-order neighbors or passing signed
messages. However, mixing high-order neighbors destroys the original graph
structure and passing signed messages utilizes an inflexible message-passing
mechanism, which is prone to producing unsatisfactory effects. To overcome the
above problems, we propose a novel GNN model based on relation vector
translation named Variational Relation Vector Graph Neural Network (VR-GNN).
VR-GNN models relation generation and graph aggregation into an end-to-end
model based on Variational Auto-Encoder. The encoder utilizes the structure,
feature and label to generate a proper relation vector. The decoder achieves
superior node representation by incorporating the relation translation into the
message-passing framework. VR-GNN can fully capture the homophily and
heterophily between nodes due to the great flexibility of relation translation
in modeling neighbor relationships. We conduct extensive experiments on eight
real-world datasets with different homophily-heterophily properties to verify
the effectiveness of our model. The experimental results show that VR-GNN gains
consistent and significant improvements against state-of-the-art GNN methods
under heterophily, and competitive performance under homophily