为了解决这个问题，Chen et al. 提出了一种structure-aware transformer，这是一种建立在新的self-attention机制上的transformer。 这种新的self-attention在计算attention之前会抽取子图的表 …

Oct 29, 2024 · 文章浏览阅读4.2k次，点赞26次，收藏32次。 本文通过对最近一年内有关 Graph Transformer 的部分顶会论文进行了简要的解读，可以看出目前该领域内的主要研究问题主要集中于如 …

Recently, there have been some applications (Grover, GraphGPS, etc) that combine transformers on graphs. In this tutorial, we will present how to build a graph transformer model via PyG.

We propose a generalization of transformer neural network architecture for arbitrary graphs: Graph Transformer. Compared to the Standard Transformer, the highlights of the presented architecture are:

5 days ago · Graph transformers are a recent advancement in machine learning, offering a new class of neural network models for graph-structured data. The synergy between transformers and graph …

Dec 6, 2024 · Graph Transformer是一种将Transformer架构应用于图结构数据的特殊神经网络模型。 该模型通过融合图神经网络 (GNNs)的基本原理与Transformer的自注意力机制，实现了对图中节点间 …

Dec 6, 2024 · 本文详细解析了Graph Transformer的技术原理、实现细节及应用场景，并通过图书推荐系统的实例，展示了其在实际问题解决中的强大能力。 Graph Transformer是一种将Transformer架构 …

Feb 23, 2025 · In light of these rapid developments, we conduct a comprehensive review of Graph Transformers, covering aspects such as their architectures, theoretical foundations, and applications …

Apr 22, 2025 · A Graph Transformer adapts the core attention mechanism of traditional Transformers to work on graph-structured data. Instead of processing sequences, it attends over nodes and edges — …

GNNs在各种基于图的任务中表现出色，但随着Graph Transformers的出现，这一领域又迎来了新的发展机遇。 Graph Transformers结合了Transformers的优势和图学习的特点，它们在自然语言处理和计 …