---
title: "CITE: Benchmarking Heterogeneous Text-Attributed Graph Models"
description: "They describe it as the largest benchmark of this kind. 70 references. Among them: 0."
authors: ["Chenghao Zhang", "Qingqing Long", "Ludi Wang", "Wenjuan Cui", "Jianjun Yu", "Yi Du"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1449/"
doi: "10.18653/v1/2026.acl-long.1449"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2026-cite-benchmarking-heterogeneous-text-attributed/"
---
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# CITE: Benchmarking Heterogeneous Text-Attributed Graph Models

**Authors:** [Chenghao Zhang](https://md.research.elamur.ai/free/en/authors/chenghao-zhang/), [Qingqing Long](https://md.research.elamur.ai/free/en/authors/qingqing-long/), Ludi Wang, Wenjuan Cui, Jianjun Yu and Yi Du · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1449/) · [PDF](https://aclanthology.org/2026.acl-long.1449.pdf) · [DOI 10.18653/v1/2026.acl-long.1449](https://doi.org/10.18653/v1/2026.acl-long.1449) · [OpenAlex](https://openalex.org/W7166842292)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary

Most text-attributed graph (TAG) benchmarks, such as Cora, CiteSeer, PubMed and Ogbn-arXiv, have only one node type. This makes it hard to fairly evaluate methods on real-world graphs, which mix several entity types and relations. To address this, the authors built CITE (Catalytic Information Textual Entities Graph), a heterogeneous citation graph covering a century of photocatalysis and electrocatalysis literature. They describe it as the largest benchmark of this kind.

- **Data sources:** paper DOIs come from Web of Science, and citation links come from OpenAlex.
- **Graph content:** the graph connects papers, authors, journals and keywords, and paper nodes carry text such as titles and abstracts.
- **Evaluation:** the authors define standard protocols for node classification and link prediction. They test four model families: homogeneous GNNs, heterogeneous GNNs, LLM-centric models and LLM+Graph models.
- **Ablations:** removing node types or text shows how much each component contributes.

The main findings are that link prediction depends mostly on graph structure rather than text, while LLM-based methods rely heavily on rich textual content. Removing some heterogeneous node types selectively can even reduce noise and help.

CITE provides a reproducible testbed for text-aware and LLM-enhanced graph learning.

## Key points

- **Scale and schema:** CITE has 438,304 nodes and 1,220,374 edges. It has four node types (Paper, Author, Journal, Keyword) and four relation types, built from publications dated 1922–2022.
- **Labels:** papers are labelled with SCI subject categories, giving 84 classes in the single-label setting and 107 in the multi-label one.
- **Setup:** experiments use a 60/20/20 split with BERT-base tokenization truncated to 128 tokens. Two time-aware splits check for temporal leakage.
- **Metrics:** node classification uses Macro-F1 and Micro-F1, and link prediction uses AUC-ROC.
- **Ablation results:** attention-based models (SimpleHGN, HGT) stay robust when node types are removed, while the meta-path model MAGNN is the most brittle. Removing abstracts barely changes link prediction but lowers TAPE's results.
- **Availability:** the data is on Hugging Face (`kg4sci/CITE`) and the code on GitHub (`kg4sci/CITE-NEW`).

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

## Institutions

- [Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) — Government body, Beijing, China · 246 works · 24,951 citations
- [University of Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/universities/university-of-chinese-academy-of-sciences/) — University, Beijing, China · 188 works · 8,743 citations
- [Computer Network Information Center](https://md.research.elamur.ai/free/en/labs/computer-network-information-center/) — Research institute, Beijing, China · 4 works · 22 citations

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## Cite

Cite as: Chenghao Zhang, Qingqing Long, Ludi Wang, Wenjuan Cui, Jianjun Yu and Yi Du. 2026. CITE: Benchmarking Heterogeneous Text-Attributed Graph Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhang2026cite,
  title = {{CITE: Benchmarking Heterogeneous Text-Attributed Graph Models}},
  author = {Chenghao Zhang, Qingqing Long, Ludi Wang, Wenjuan Cui, Jianjun Yu and Yi Du},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1449},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1449}
}
```

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*Updated 2 November 2026*