GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph
This work addresses the limitation of independent modeling in textual graph representation learning, offering a novel architecture for researchers in graph-based NLP.
The authors tackled the problem of representation learning on textual graphs by proposing GraphFormers, which integrates graph neural networks with transformer blocks to fuse text encoding and graph aggregation, achieving state-of-the-art performance on three large-scale benchmark datasets.
The representation learning on textual graph is to generate low-dimensional embeddings for the nodes based on the individual textual features and the neighbourhood information. Recent breakthroughs on pretrained language models and graph neural networks push forward the development of corresponding techniques. The existing works mainly rely on the cascaded model architecture: the textual features of nodes are independently encoded by language models at first; the textual embeddings are aggregated by graph neural networks afterwards. However, the above architecture is limited due to the independent modeling of textual features. In this work, we propose GraphFormers, where layerwise GNN components are nested alongside the transformer blocks of language models. With the proposed architecture, the text encoding and the graph aggregation are fused into an iterative workflow, {making} each node's semantic accurately comprehended from the global perspective. In addition, a {progressive} learning strategy is introduced, where the model is successively trained on manipulated data and original data to reinforce its capability of integrating information on graph. Extensive evaluations are conducted on three large-scale benchmark datasets, where GraphFormers outperform the SOTA baselines with comparable running efficiency.