Are More Layers Beneficial to Graph Transformers?
This addresses a bottleneck in graph transformer design, enabling deeper models for improved performance in graph-based tasks.
The paper tackles the problem of graph transformers being limited in depth due to vanishing capacity of global attention, and proposes DeepGraph, which uses substructure tokens and local attention to achieve state-of-the-art performance on various graph benchmarks with deeper models.
Despite that going deep has proven successful in many neural architectures, the existing graph transformers are relatively shallow. In this work, we explore whether more layers are beneficial to graph transformers, and find that current graph transformers suffer from the bottleneck of improving performance by increasing depth. Our further analysis reveals the reason is that deep graph transformers are limited by the vanishing capacity of global attention, restricting the graph transformer from focusing on the critical substructure and obtaining expressive features. To this end, we propose a novel graph transformer model named DeepGraph that explicitly employs substructure tokens in the encoded representation, and applies local attention on related nodes to obtain substructure based attention encoding. Our model enhances the ability of the global attention to focus on substructures and promotes the expressiveness of the representations, addressing the limitation of self-attention as the graph transformer deepens. Experiments show that our method unblocks the depth limitation of graph transformers and results in state-of-the-art performance across various graph benchmarks with deeper models.