用大模型自动设计图神经网络架构，省去手动调代码。
LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search
- 通过修改提示词让大模型适配新图结构搜索空间。
- 在同质与异质图任务上均优于现有方法。
- 适合想快速部署GNN架构搜索的研究者使用。
图神经架构搜索（GNAS）可自动设计针对特定下游图学习任务的图神经网络（GNN）。然而，现有GNAS方法常需手动调整以适应新图搜索空间，需大量代码优化和领域知识。为此，我们提出LLM4GNAS，一个基于大语言模型（LLM）的GNAS工具包。该工具包包含基于LLM的图神经架构搜索算法库，可通过修改LLM提示词实现算法对新搜索空间的快速适配，显著减少人工干预与代码修改。其具备可扩展性与鲁棒性，集成LLM增强的图特征工程、LLM增强的图神经架构搜索及LLM增强的超参数优化。实验表明，LLM4GNAS在同质与异质图任务上均优于现有GNAS方法。
原文摘要 · Abstract (English)
Graph Neural Architecture Search (GNAS) facilitates the automatic design of Graph Neural Networks (GNNs) tailored to specific downstream graph learning tasks. However, existing GNAS approaches often require manual adaptation to new graph search spaces, necessitating substantial code optimization and domain-specific knowledge. To address this challenge, we present LLM4GNAS, a toolkit for GNAS that leverages the generative capabilities of Large Language Models (LLMs). LLM4GNAS includes an algorithm library for graph neural architecture search algorithms based on LLMs, enabling the adaptation of GNAS methods to new search spaces through the modification of LLM prompts. This approach reduces the need for manual intervention in algorithm adaptation and code modification. The LLM4GNAS toolkit is extensible and robust, incorporating LLM-enhanced graph feature engineering, LLM-enhanced graph neural architecture search, and LLM-enhanced hyperparameter optimization. Experimental results indicate that LLM4GNAS outperforms existing GNAS methods on tasks involving both homogeneous and heterogeneous graphs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。