AGFormer: Efficient Graph Representation with Anchor-Graph Transformer
Bo Jiang Fei Xu Ziyan Zhang Jin Tang Feiping Nie
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.