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.

本研究提出了一种新的图形 Transformer 架构，称为 Anchor Graph Transformer（AGFormer），通过利用锚图模型将节点到节点的消息传递转换为锚到锚和锚到节点的消息传递过程，从而比常规节点到节点的 Transformer 更高效且更具鲁棒性。

AGFormer: 锚点图变换器实现高效图表示

AGFormer: Efficient Graph Representation with Anchor-Graph Transformer

Recently, graph convolutional network (GCN) has been widely used for
semi-supervised classification and deep feature representation on
graph-structured data. However, existing GCN generally fails to consider the
local invariance constraint in learning and representation process. That is, if
two data points Xi and Xj are close in the intrinsic geometry of the data
distribution, then their labels/representations should also be close to each
other. This is known as local invariance assumption which plays an essential
role in the development of various kinds of traditional algorithms, such as
dimensionality reduction and semi-supervised learning, in machine learning
area. To overcome this limitation, we introduce a graph Laplacian GCN (gLGCN)
approach for graph data representation and semi-supervised classification. The
proposed gLGCN model is capable of encoding both graph structure and node
features together while maintains the local invariance constraint naturally for
robust data representation and semi-supervised classification. Experiments show
the benefit of the benefits the proposed gLGCN network.

本文提出了一种图拉普拉斯图卷积网络 (gLGCN) 方法，该方法通过编码图结构和节点特征同时保持局部不变性约束，用于图数据表示和半监督分类，并在实验中证明了其有效性。