Refining High-Quality Labels Using Large Language Models to Enhance Node Classification in Graph Echo State Network

Ikhlas Bargougui, Ikhlas Bargougui, Rebh Soltani, Hela Ltifi, Hela Ltifi

2025

Abstract

Graph learning has attracted significant attention due to its applicability in various real-world scenarios involving textual data. Recent advancements, such as Graph Echo State Networks (GESN) within the reservoir computing (RC) paradigm, have shown notable success in node-level classification tasks, especially for heterophilic graphs. However, graph neural networks (GNNs) suffer from the need for a large number of high-quality labels to achieve promising performance. Conversely, large language models (LLMs), with their extensive knowledge bases, have demonstrated impressive zero-shot and few-shot learning abilities, particularly for node classification tasks. However, LLMs struggle with efficiently processing structural data and incur high inference costs. In this paper, we introduce a novel pipeline named LLM-GESN, which involves four flexible components: k-means clustering for active node selection, LLM for difficulty aware annotation, adaptable post-selection, and GESN model training and prediction. Experimental results demonstrate the effectiveness of LLM-GESN on text-attributed graphs from the Cora, CiteSeer, Pubmed, Wikics, and ogbn-arxiv datasets. Our LLM-GESN achieved significant test accuracy of 86.67%, 76.63%, 74.58%, 77.09%, and 58.79%, respectively, compared to state-of-the-art methods.

Paper Citation

in Harvard Style

Bargougui I., Soltani R. and Ltifi H. (2025). Refining High-Quality Labels Using Large Language Models to Enhance Node Classification in Graph Echo State Network. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 555-565. DOI: 10.5220/0013220600003890

in Bibtex Style

@conference{icaart25,
author={Ikhlas Bargougui and Rebh Soltani and Hela Ltifi},
title={Refining High-Quality Labels Using Large Language Models to Enhance Node Classification in Graph Echo State Network},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={555-565},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013220600003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Refining High-Quality Labels Using Large Language Models to Enhance Node Classification in Graph Echo State Network
SN - 978-989-758-737-5
AU - Bargougui I.
AU - Soltani R.
AU - Ltifi H.
PY - 2025
SP - 555
EP - 565
DO - 10.5220/0013220600003890
PB - SciTePress