Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models

• URL: http://arxiv.org/abs/2407.13989v3
• Date: Wed, 4 Sep 2024 17:52:37 GMT
• Title: Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models
• Authors: Quan Li, Tianxiang Zhao, Lingwei Chen, Junjie Xu, Suhang Wang,
• Abstract summary: Graph neural networks (GNNs) have great ability in node classification, a fundamental task on graphs. We propose a novel approach that integrates Large Language Models (LLMs) and GNNs. Our model in improving node classification accuracy with considerably limited labeled data, surpassing state-of-the-art baselines by significant margins.
• Score: 30.867447814409623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graphs are pervasive in the real-world, such as social network analysis, bioinformatics, and knowledge graphs. Graph neural networks (GNNs) have great ability in node classification, a fundamental task on graphs. Unfortunately, conventional GNNs still face challenges in scenarios with few labeled nodes, despite the prevalence of few-shot node classification tasks in real-world applications. To address this challenge, various approaches have been proposed, including graph meta-learning, transfer learning, and methods based on Large Language Models (LLMs). However, traditional meta-learning and transfer learning methods often require prior knowledge from base classes or fail to exploit the potential advantages of unlabeled nodes. Meanwhile, LLM-based methods may overlook the zero-shot capabilities of LLMs and rely heavily on the quality of generated contexts. In this paper, we propose a novel approach that integrates LLMs and GNNs, leveraging the zero-shot inference and reasoning capabilities of LLMs and employing a Graph-LLM-based active learning paradigm to enhance GNNs' performance. Extensive experiments demonstrate the effectiveness of our model in improving node classification accuracy with considerably limited labeled data, surpassing state-of-the-art baselines by significant margins.

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