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

TL;DR

E2EG is an end-to-end node classification model that integrates graph topology and text attributes, improving accuracy and efficiency over prior two-stage methods like GIANT, with reduced parameters and training time.

Contribution

The paper introduces E2EG, a novel end-to-end framework that combines graph structure and text attributes for node classification, eliminating the need for separate embedding and classification stages.

Findings

E2EG achieves slightly better accuracy (+0.5%) than GIANT+MLP on ogbn-arxiv.

E2EG reduces model training time by up to 40%.

E2EG outperforms GIANT+MLP in inductive settings by up to +2.23%.

Abstract

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%.