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How to Make LMs Strong Node Classifiers?

Paper · 2026 · Zhe Xu, Kaveh Hassani, Si Zhang et al.

Summary

The paper asks whether standard, instruction-tuned text-to-text LMs can classify nodes in Text-Attributed Graphs (TAGs) as well as specialized graph models, without changing the LM's architecture. Earlier LM-based approaches such as InstructGLM and LLaGA inject graph information through new tokens or projected embeddings. This blocks multi-dataset training and rules out fine-tuning services that hide the model's internals. The authors propose AugLM (Augmented Graph Language Model), which only enriches the textual prompt. It has two parts. First, a retrieval step selects context nodes, using Personalized PageRank for topological relevance and class-prototype matching for semantic relevance. Second, a small pre-trained GNN narrows the set of candidate labels that the LM chooses from. On Cora, Pubmed, ogbn-arxiv and ogbn-products, Flan-T5 backbones equipped with AugLM outperform previous text-output node classifiers and come close to the best vector-output models. One model trained jointly on all four graphs performs about as well as separately trained models. This suggests that general LMs could serve as unified graph learners.

Key points

  • Accuracy with Flan-T5-large: AugLM reaches 91.51% on Cora, 95.16% on Pubmed, 76.00% on ogbn-arxiv and 82.90% on ogbn-products. The last figure beats TAPE + RevGAT (82.34%), while TAPE + RevGAT remains stronger on Cora, Pubmed and ogbn-arxiv.
  • Ablation (T5-small): Removing GNN-based label pruning causes the largest drop on Cora, with accuracy falling from 91.14% to 85.52%. Each of the three components contributes on every dataset.
  • Joint training (Flan-T5-small, all four TAGs): Results are comparable to separate training and even slightly better on Cora (91.52 vs 91.14) and ogbn-products (82.29 vs 81.73).
  • Baselines: Text-output baselines include InstructGLM, LLaGA, GraphPrompter and GraphICL. Vector-output baselines include GCN, GraphSAGE, GLEM, GIANT, TAPE, SimTeG and OFA.
  • Limitations: Prompt templates are written by hand, and a GNN must be pre-trained for each dataset, which rules out zero-shot use.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksPhysics and Astronomy · 52 worksStatistical and Nonlinear Physics · 47 works

Cite

Cite as Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long and Hanghang Tong. 2026. How to Make LMs Strong Node Classifiers?. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-eacl.14

@inproceedings{xu2026how,
  title = {{How to Make LMs Strong Node Classifiers?}},
  author = {Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long and Hanghang Tong},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.14},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.14}
}

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