LiteGT: Efficient and Lightweight Graph Transformers

Abstract

Transformers have shown great potential for modeling long-term dependencies for natural language processing and computer vision. However, little study has applied transformers to graphs, which is challenging due to the poor scalability of the attention mechanism and the under-exploration of graph inductive bias. To bridge this gap, we propose a Lite Graph Transformer (LiteGT) that learns on arbitrary graphs efficiently. First, a node sampling strategy is proposed to sparsify the considered nodes in self-attention with only $mathcal{O}(Nlog N)$ time. Second, we devise two kernelization approaches to form two-branch attention blocks, which not only leverage graph-specific topology information, but also reduce computation further to $mathcal{O}(frac{1}{2}Nlog N)$. Third, the nodes are updated with different attention schemes during training, thus largely mitigating over-smoothing problems when the model layers deepen. Extensive experiments demonstrate that LiteGT achieves competitive performance on both extit{node classification} and extit{link prediction} on datasets with millions of nodes. Specifically, extit{Jaccard + Sampling + Dim. reducing} setting reduces more than $100 imes$ computation and halves the model size without performance degradation.