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.

本文旨在研究图转换器的深度问题，发现当前的图转换器深度受到全局注意力瓶颈的制约，限制其对关键子结构和表达特征的关注。作者提出了一种名为DeepGraph的新型图转换器模型，结合子结构记号和局部注意力，以提高全局注意力对子结构的关注能力和表达能力，解决了自注意力深度问题，取得了在各种图形基准测试中的最新成果。

图形转换器是否受益于更多层？