---
title: "Can Graph Descriptive Order Affect Solving Graph Problems with"
description: "Studies of LLM graph reasoning usually turn a graph into text by listing its edges in an arbitrary… 56 references, cited by 7 papers here."
authors: ["Yuyao Ge", "Shenghua Liu", "Baolong Bi", "Yiwei Wang", "Lingrui Mei", "Wenjie Feng", "Lizhe Chen", "Xueqi Cheng"]
year: 2025
venue: "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2025.acl-long.321/"
doi: "10.18653/v1/2025.acl-long.321"
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/ge-2025-can-graph-descriptive-order-affect/"
---
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# Can Graph Descriptive Order Affect Solving Graph Problems with LLMs?

**Authors:** [Yuyao Ge](https://md.research.elamur.ai/free/en/authors/yuyao-ge/), [Shenghua Liu](https://md.research.elamur.ai/free/en/authors/shenghua-liu/), [Baolong Bi](https://md.research.elamur.ai/free/en/authors/baolong-bi/), [Yiwei Wang](https://md.research.elamur.ai/free/en/authors/yiwei-wang/), [Lingrui Mei](https://md.research.elamur.ai/free/en/authors/lingrui-mei/), Wenjie Feng, Lizhe Chen and [Xueqi Cheng](https://md.research.elamur.ai/free/en/authors/xueqi-cheng/) · **Year:** 2025 · **Venue:** [Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/)) · **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/2025.acl-long.321/) · [PDF](https://aclanthology.org/2025.acl-long.321.pdf) · [DOI 10.18653/v1/2025.acl-long.321](https://doi.org/10.18653/v1/2025.acl-long.321) · [OpenAlex](https://openalex.org/W4412945551)

**Citations:** 3 citations (OpenAlex, 2026-10-14) · [7](https://md.research.elamur.ai/free/en/papers/ge-2025-can-graph-descriptive-order-affect/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Studies of LLM graph reasoning usually turn a graph into text by listing its edges in an arbitrary sequence. This paper asks whether the sequence of those edges changes how well models solve graph tasks. The authors compare a random edge order with four structured ones. Two come from graph traversal (BFS and DFS) and two from node ranking (PageRank and Personalized PageRank). Graphs are written as adjacency-style edge lists. To make sure traversal-based orders cover every edge, the traversal runs over the dual graph. The authors test six tasks, from local checks like connectivity to global problems like Hamilton paths, plus node classification on labeled graphs. They use six LLMs and several prompt styles. Structured orders consistently help models understand graph structure, but the size of the gain depends on the task and its nature. The authors trace this sensitivity to positional "attention bias." The work matters because edge order is a cheap, easy-to-overlook factor in prompting. It also releases a benchmark, GraphDO.

## Key points
- **GraphDO dataset:** 8,500 cases, each with an ordered graph description, a question, and an answer. Graphs for the classic structural tasks are generated with the Erdős-Rényi method.
- **Six tasks:** connectivity, cycle detection, Hamilton path, shortest path (weighted), topological sort (directed), and node classification.
- **Example gain:** on cycle detection, BFS-ordered descriptions raised accuracy from 64.50% (random order) to 72.71%. The paper reports this as an average improvement of 12.73%.
- **Models:** the main experiments use GPT-3.5-Turbo-0613. The trends are replicated on Qwen2-7B, Mistral-7B, LLaMA2-7B, LLaMA2-13B and Vicuna-7B, though the effect is weaker on these open models. Qwen2-7B stands out on connectivity, outperforming LLaMA2-13B-Chat.
- **Stated limitations:** the paper offers no theoretical explanation for the effect. It also does not deeply analyze how the effect varies across graph types or structures.

*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

- [Tsinghua University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) — University, Beijing, China · 333 works · 87,019 citations
- [University of Science and Technology of China](https://md.research.elamur.ai/free/en/universities/university-of-science-and-technology-of-china/) — University, Hefei, China · 108 works · 112,698 citations
- [Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) — Government body, Beijing, China · 194 works · 23,906 citations
- [University of Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/universities/university-of-chinese-academy-of-sciences/) — University, Beijing, China · 147 works · 8,480 citations
- [Institute of Computing Technology](https://md.research.elamur.ai/free/en/labs/institute-of-computing-technology/) — Research institute, Beijing, China · 51 works · 3,411 citations
- [University of California, Merced](https://md.research.elamur.ai/free/en/universities/university-of-california-merced/) — University, Merced, United States · 11 works · 425 citations

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## Cite

Cite as: Yuyao Ge, Shenghua Liu, Baolong Bi, Yiwei Wang, Lingrui Mei, Wenjie Feng, Lizhe Chen and Xueqi Cheng. 2025. Can Graph Descriptive Order Affect Solving Graph Problems with LLMs?. Proceedings of the 63rd 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{ge2025can,
  title = {{Can Graph Descriptive Order Affect Solving Graph Problems with LLMs?}},
  author = {Yuyao Ge, Shenghua Liu, Baolong Bi, Yiwei Wang, Lingrui Mei, Wenjie Feng, Lizhe Chen and Xueqi Cheng},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.321},
  url = {https://doi.org/10.18653/v1/2025.acl-long.321}
}
```

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*Updated 31 October 2026*