Papers › E2EG: End-to-End Node Classification Using Graph Topology and Text-based Node Attributes
E2EG: End-to-End Node Classification Using Graph Topology and Text-based Node Attributes
Tu Anh Dinh, Jeroen den Boef, Joran Cornelisse, Paul Groth
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%.
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Code
Syntology Ran 5 of 9 code samples harvested from 1 repository linked to this paper; 4 have no recorded run. Of those that ran: 1 ran · honoured contract; 2 ran · our draft was wrong; 2 ran with no contract checked.
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Code Syntology ran Syntology
9 samples harvested; 5 ran; 1 honoured the contract we drafted; 4 have no recorded run. Read from Syntology's graph 2026-09-25; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Property Prediction | ogbn-arxiv | E2EG (use raw text) | Ext. data | Yes | #42 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | E2EG (use raw text) | Number of params | 83724841 | #42 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | E2EG (use raw text) | Test Accuracy | 0.7362 ± 0.0014 | #42 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | E2EG (use raw text) | Validation Accuracy | 0.7487 ± 0.0011 | #42 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-products | E2EG (use raw text) | Ext. data | Yes | #41 of 64 | Archive leaderboard | report |
| Node Property Prediction | ogbn-products | E2EG (use raw text) | Number of params | 66793520 | #41 of 64 | Archive leaderboard | report |
| Node Property Prediction | ogbn-products | E2EG (use raw text) | Test Accuracy | 0.8098 ± 0.0040 | #41 of 64 | Archive leaderboard | report |
| Node Property Prediction | ogbn-products | E2EG (use raw text) | Validation Accuracy | 0.9234 ± 0.0009 | #41 of 64 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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