---
title: "Paths-over-Graph — paper by Tan et al. (2024)"
description: "The authors propose Paths-over-Graph (PoG), a plug-and-play framework that feeds LLMs whole… 54 references, cited by 14 papers here."
authors: ["Xingyu Tan", "Xiaoyang Wang", "Qing Liu", "Xiwei Xu", "Xin Yuan", "Wenjie Zhang"]
year: 2024
venue: "arXiv:2410.14211"
source: "https://arxiv.org/abs/2410.14211"
doi: "10.48550/arxiv.2410.14211"
arxiv: "2410.14211"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tan-2024-paths-over-graph-knowledge-graph/"
---
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# Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning

**Authors:** Xingyu Tan, [Xiaoyang Wang](https://md.research.elamur.ai/free/en/authors/xiaoyang-wang/), [Qing Liu](https://md.research.elamur.ai/free/en/authors/qing-liu/), Xiwei Xu, Xin Yuan and [Wenjie Zhang](https://md.research.elamur.ai/free/en/authors/wenjie-zhang/) · **Year:** 2024 · **Venue:** arXiv:2410.14211 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2410.14211) · [PDF](https://arxiv.org/pdf/2410.14211) · [DOI 10.48550/arxiv.2410.14211](https://doi.org/10.48550/arxiv.2410.14211) · [arXiv 2410.14211](https://arxiv.org/abs/2410.14211) · [OpenAlex](https://openalex.org/W4409670780)

**Citations:** 66 citations (OpenAlex, 2026-10-14) · [14](https://md.research.elamur.ai/free/en/papers/tan-2024-paths-over-graph-knowledge-graph/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
LLMs often hallucinate or lack facts when questions need several reasoning steps. Existing KG-augmented methods such as ToG and MindMap struggle with three things: multi-hop chains, questions that mention several entities, and the structure of the graph itself. The authors propose Paths-over-Graph (PoG), a plug-and-play framework that feeds LLMs whole reasoning paths from a knowledge graph instead of isolated triples.

PoG works in four stages:
- **Planning.** The LLM extracts topic entities, splits the question into sub-questions and estimates how many hops separate each entity from the answer.
- **Subgraph reduction.** A question subgraph is built and shrunk with clustering and reduction.
- **Path exploration.** Paths linking all topic entities are explored in three phases, starting from the predicted depth.
- **Pruning and answering.** Candidate paths are filtered by beam-search pruning that combines graph structure, LLM prompting and SBERT similarity, then summarized before the final answer.

On five KGQA benchmarks, PoG beats ToG by 18.9% accuracy on average across GPT-3.5-Turbo and GPT-4. This makes answers more faithful and interpretable, and knowledge can be updated through the KG without retraining the model.

## Key points
- Evaluated on CWQ, WebQSP, GrailQA, WebQuestions and SimpleQuestions with GPT-3.5-Turbo and GPT-4. The paper appeared at WWW '25.
- PoG running on GPT-3.5-Turbo outperforms ToG running on GPT-4 by as much as 23.9%.
- Graph pruning removes up to 54% of entities before exploration on CWQ, cutting the average from about 3.54M to 1.62M.
- On multi-entity questions, accuracy reaches 93.1% on WebQSP. Exploration depth is capped at 3, since deeper search brought little benefit.
- An efficient variant cuts LLM token usage by over 50% while staying within about ±2% of the best configuration's accuracy.

*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

- [Data61](https://md.research.elamur.ai/free/en/labs/data61/) — Research institute, Sydney, Australia · 16 works · 2,532 citations
- [UNSW Sydney](https://md.research.elamur.ai/free/en/universities/unsw-sydney/) — University, Sydney, Australia · 34 works · 1,509 citations
- [Commonwealth Scientific and Industrial Research Organisation](https://md.research.elamur.ai/free/en/government/commonwealth-scientific-and-industrial-research-organisation/) — Government body, Canberra, Australia · 15 works · 1,898 citations

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## Cite

Cite as: Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Xin Yuan and Wenjie Zhang. 2024. Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning. arXiv:2410.14211.

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

```bibtex
@misc{tan2024paths,
  title = {{Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning}},
  author = {Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Xin Yuan and Wenjie Zhang},
  year = {2024},
  doi = {10.48550/arxiv.2410.14211},
  eprint = {2410.14211},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2410.14211}
}
```

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*Updated 30 October 2026*