Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution
This work addresses the challenge of reducing manual effort in GNN design for network decision-making, though it appears incremental as it builds on existing methods like GNNs and LLMs.
The paper tackles the problem of automating Graph Neural Network (GNN) configuration and tuning by proposing LLMNet, a system that uses Large Language Models with knowledge-guided evolution to design GNNs, achieving effectiveness across twelve datasets in three graph learning tasks.
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.