一文带你浏览Graph Transformers - 知乎 - 知乎专栏
为什么图上要使用Transformer？ 简要提一下GT带来的好处： 能捕获长距离依赖; 2. 减轻出现过平滑，过挤压现象. 3. GT中甚至可以结合进GNN以及频域信息（Laplacian PE），模型会有更强 …
GNN、Transformer与Graph Transformer的关系? - 知乎专栏
2023年3月3日 · 篇章1先从看看目前Graph注意力中比较火的方法吧，也就是Graph 与Transformer的结合。 那Graph Tranformer与GNN和Transformer之间的区别与关联是怎么样 …
Graph Transformer: A Generalization of Transformers to Graphs
2021年3月4日 · We find that attention using graph sparsity and positional encodings are two key design aspects for the generalization of transformers to arbitrary graphs. Now, we discuss …
Retrofitting Temporal Graph Neural Networks with Transformer
2024年9月9日 · As such, we propose methods to parallelize the CSR format conversion and graph sampling. We also adapt Transformer codebase to train TF-TGN efficiently with multiple …
Transformer for Graphs: An Overview from Architecture Perspective
2022年2月17日 · It's imperative to sort out the existing Transformer models for graphs and systematically investigate their effectiveness on various graph tasks. In this survey, we provide …
Understanding Transformer reasoning capabilities via graph …
2024年12月20日 · We provide a comprehensive evaluation of transformer models’ graph reasoning capabilities and demonstrate that they often outperform more domain-specific graph …
A simple and efficient graph Transformer architecture for …
2023年10月5日 · Replace the self-attention layer in Transformer with graph convolution for improved local representation learning. A novel fused framework is proposed which combine …
Non-stationary GNNCrossformer: Transformer with graph …
2025年1月1日 · To solve these problems, this paper proposes a novel Transformer-based model named Non-stationary GNNCrossformer for non-stationary multivariate Spatio-Temporal …
We begin by reviewing the current work analyzing the standard Transformer architecture, focusing on training dy-namics and expressiveness. Then, we move on to discuss the theoretical …
When Transformer Meets Large Graphs: An Expressive and …
Our graph isomorphism analysis shows the complementary natures of GNN and Transformer, justifying the motivation and design of Coarformer. We conduct extensive experiments on real …