Graph Quaternion-Valued Attention Networks for Node Classification

Node classification is a prominent graph-based task and various Graph neural networks (GNNs) models have been applied for solving it. In this paper, we introduce a novel GNN architecture for node classification called Graph Quaternion-Valued Attention Networks (GQAT), which enhances the original graph attention networks by replacing the vector multiplication in self-attention with quaternion vector multiplication.