用大模型处理图学习任务，发现其在少样本下表现远超传统方法。
Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
- 用自然语言描述图，让大模型直接推理图任务
- 指令微调的大模型在少样本下准确率显著高于传统图模型
- 适合对图结构理解、跨领域迁移有需求的研究者
近年来，大语言模型（LLMs）成为图任务的有力候选。尽管多数研究利用自然语言描述图并使用LLMs进行推理，但多局限于性能对比，缺乏与传统图学习模型的全面比较及潜力探索。本文系统评估了现成与指令微调的LLMs在多种场景下的表现，涵盖准确性、数据泄露风险、计算开销，并考察其在少样本/零样本设置、领域迁移、结构理解与鲁棒性方面的表现。结果表明，经过指令微调的LLMs在少样本条件下显著优于传统图学习模型，具备强大的领域迁移能力、泛化性能与鲁棒性。研究揭示了LLMs在图学习中的广泛潜力，为后续研究奠定基础。
原文摘要 · Abstract (English)
In recent years, large language models (LLMs) have emerged as promising candidates for graph tasks. Many studies leverage natural language to describe graphs and apply LLMs for reasoning, yet most focus narrowly on performance benchmarks without fully comparing LLMs to graph learning models or exploring their broader potential. In this work, we present a comprehensive study of LLMs on graph learning tasks, evaluating both off-the-shelf and instruction-tuned models across a variety of scenarios. Beyond accuracy, we discuss data leakage concerns and computational overhead, and assess their performance under few-shot/zero-shot settings, domain transfer, structural understanding, and robustness. Our findings show that LLMs, particularly those with instruction tuning, greatly outperform traditional graph learning models in few-shot settings, exhibit strong domain transferability, and demonstrate excellent generalization and robustness. Our study highlights the broader capabilities of LLMs in graph learning and provides a foundation for future research.
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