Technology Category
Machine Learning: Graph-based Machine LearningData Mining & Knowledge Management: Graph Mining, Social Network Analysis & CommunitySearch and Optimization: Learning to Search
Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution
🤖 AI Summary
To address the low efficiency and poor generalizability of manual configuration and tuning of Graph Neural Networks (GNNs), this paper proposes a knowledge-guided, LLM-driven automated GNN design framework. Methodologically, it constructs a structured graph learning knowledge base and integrates Retrieval-Augmented Generation (RAG) with a multi-agent cooperative evolutionary mechanism, enabling end-to-end autonomous design and optimization of GNN architectures, hyperparameters, and training strategies by large language models. Its core contribution is the introduction of the first “knowledge–retrieval–evolution” closed-loop paradigm, explicitly incorporating domain-specific knowledge into the AutoML pipeline for GNNs. Extensive experiments across 12 benchmark datasets and three graph learning tasks demonstrate that the proposed method achieves an average performance gain of 12.7% over manually tuned GNNs while reducing configuration time by 90%, significantly enhancing both automation capability and cross-task generalizability.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Automating GNN configuration using LLMs for network decision-making
Enhancing GNN design through knowledge-guided evolution and RAG
Improving graph learning tasks with specialized knowledge bases
Innovation
Methods, ideas, or system contributions that make the work stand out.
LLMNet automates GNN design using Large Language Models
Agents build knowledge bases for graph-related tasks
Retrieval-Augmented Generation refines GNN models automatically
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