用大模型自动设计图神经网络，减少人工调参
Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution
- 用大模型构建知识库，自动配置图神经网络
- 在12个数据集上三种任务表现优异
- 适合需要快速部署GNN的开发者
有效的网络决策常依赖于图结构数据的学习，图神经网络（GNN）在此中扮演核心角色，但其配置与调优需大量人力。本演示提出LLMNet，展示如何通过大型语言模型（LLM）实现GNN的自动化设计。系统构建一组智能体，建立与图相关的知识库，并利用检索增强生成（RAG）技术，通过知识引导的演化过程支持GNN模型的自动配置与优化。这些智能体结合专用知识库，通过与知识库交互，提取任务与图结构的洞察。实验结果表明，LLMNet在12个数据集、三个图学习任务上均表现卓越，验证了其在GNN设计中的有效性。
原文摘要 · Abstract (English)
Effective decision-making on networks often relies on learning from graph-structured data, where Graph Neural Networks (GNNs) play a central role, but they take efforts to configure and tune. In this demo, we propose LLMNet, showing how to design GNN automated through Large Language Models. Our system develops a set of agents that construct graph-related knowlege bases and then leverages Retrieval-Augmented Generation (RAG) to support automated configuration and refinement of GNN models through a knowledge-guided evolution process. These agents, equipped with specialized knowledge bases, extract insights into tasks and graph structures by interacting with the knowledge bases. Empirical results show LLMNet excels in twelve datasets across three graph learning tasks, validating its effectiveness of GNN model designing.
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