To alleviate the local receptive issue of GCN, Transformers have been exploited to capture the long range dependences of nodes for graph data representation and learning. However, existing graph Transformers generally employ regular self-attention module for all node-to-node message passing which needs to learn the affinities/relationships between all node's pairs, leading to high computational cost issue. Also, they are usually sensitive to graph noises. To overcome this issue, we propose a novel graph Transformer architecture, termed Anchor Graph Transformer (AGFormer), by leveraging an anchor graph model. To be specific, AGFormer first obtains some representative anchors and then converts node-to-node message passing into anchor-to-anchor and anchor-to-node message passing process. Thus, AGFormer performs much more efficiently and also robustly than regular node-to-node Transformers. Extensive experiments on several benchmark datasets demonstrate the effectiveness and benefits of proposed AGFormer.

翻译：为缓解图卷积网络（GCN）的局部感受野问题，研究者利用Transformer捕获图数据表征与学习中节点间的长程依赖关系。然而，现有图Transformer通常采用常规自注意力机制进行节点间全消息传递，需学习所有节点对的关联/关系，导致计算成本高昂。此外，这类模型对图噪声敏感。为解决该问题，我们提出一种新型图Transformer架构——锚点图Transformer（AGFormer），其核心在于利用锚点图模型。具体而言，AGFormer首先获取若干代表性锚点，进而将节点间消息传递过程转化为锚点间及锚点与节点间的消息传递过程。因此，AGFormer相比常规节点间Transformer具有更高效率和更强鲁棒性。在多个基准数据集上的大量实验证明了所提AGFormer的有效性与优势。