  Graphs have emerged as critical data structures for content analysis in
various domains, such as social network analysis, bioinformatics, and
recommendation systems. Node classification, a fundamental task in this
context, is typically tackled using graph neural networks (GNNs).
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.
