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The representation learning on textual graph is to generate low-dimensionalembeddings for the nodes based on the individual textual features and theneighbourhood information. Recent breakthroughs on pretrained language modelsand graph neural networks push forward the development of correspondingtechniques. The existing works mainly rely on the cascaded model architecture:the textual features of nodes are independently encoded by language models atfirst; the textual embeddings are aggregated by graph neural networksafterwards. However, the above architecture is limited due to the independentmodeling of textual features. In this work, we propose GraphFormers, wherelayerwise GNN components are nested alongside the transformer blocks oflanguage models. With the proposed architecture, the text encoding and thegraph aggregation are fused into an iterative workflow, {making} each node'ssemantic accurately comprehended from the global perspective. In addition, a{progressive} learning strategy is introduced, where the model is successivelytrained on manipulated data and original data to reinforce its capability ofintegrating information on graph. Extensive evaluations are conducted on threelarge-scale benchmark datasets, where GraphFormers outperform the SOTAbaselines with comparable running efficiency.

GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph