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Node classification utilizing text-based node attributes has many real-worldapplications, ranging from prediction of paper topics in academic citationgraphs to classification of user characteristics in social media networks.State-of-the-art node classification frameworks, such as GIANT, use a two-stagepipeline: first embedding the text attributes of graph nodes then feeding theresulting embeddings into a node classification model. In this paper, weeliminate these two stages and develop an end-to-end node classification modelthat builds upon GIANT, called End-to-End-GIANT (E2EG). The tandem utilizationof a main and an auxiliary classification objectives in our approach results ina more robust model, enabling the BERT backbone to be switched out for adistilled encoder with a 25% - 40% reduction in the number of parameters.Moreover, the model's end-to-end nature increases ease of use, as it avoids theneed of chaining multiple models for node classification. Compared to aGIANT+MLP baseline on the ogbn-arxiv and ogbn-products datasets, E2EG obtainsslightly better accuracy in the transductive setting (+0.5%), while reducingmodel training time by up to 40%. Our model is also applicable in the inductivesetting, outperforming GIANT+MLP by up to +2.23%.

E2EG: End-to-End Node Classification Using Graph Topology and Text-based Node Attributes