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Can we combine heterogenous graph structure with text to learn high-qualitysemantic and behavioural representations? Graph neural networks (GNN)s encodenumerical node attributes and graph structure to achieve impressive performancein a variety of supervised learning tasks. Current GNN approaches arechallenged by textual features, which typically need to be encoded to anumerical vector before provided to the GNN that may incur some informationloss. In this paper, we put forth an efficient and effective framework termedlanguage model GNN (LM-GNN) to jointly train large-scale language models andgraph neural networks. The effectiveness in our framework is achieved byapplying stage-wise fine-tuning of the BERT model first with heterogenous graphinformation and then with a GNN model. Several system and design optimizationsare proposed to enable scalable and efficient training. LM-GNN accommodatesnode and edge classification as well as link prediction tasks. We evaluate theLM-GNN framework in different datasets performance and showcase theeffectiveness of the proposed approach. LM-GNN provides competitive results inan Amazon query-purchase-product application.

Efficient and effective training of language and graph neural network models