  Node classification utilizing text-based node attributes has many real-world
applications, ranging from prediction of paper topics in academic citation
graphs to classification of user characteristics in social media networks.
State-of-the-art node classification frameworks, such as GIANT, use a two-stage
pipeline: first embedding the text attributes of graph nodes then feeding the
resulting embeddings into a node classification model. In this paper, we
eliminate these two stages and develop an end-to-end node classification model
that builds upon GIANT, called End-to-End-GIANT (E2EG). The tandem utilization
of a main and an auxiliary classification objectives in our approach results in
a more robust model, enabling the BERT backbone to be switched out for a
distilled 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 the
need of chaining multiple models for node classification. Compared to a
GIANT+MLP baseline on the ogbn-arxiv and ogbn-products datasets, E2EG obtains
slightly better accuracy in the transductive setting (+0.5%), while reducing
model training time by up to 40%. Our model is also applicable in the inductive
setting, outperforming GIANT+MLP by up to +2.23%.
