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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.14529 |
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| _version_ | 1866912436269875200 |
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| author | Zheng, Xiaohan Wei, Lanning Li, Yong Yao, Quanming |
| author_facet | Zheng, Xiaohan Wei, Lanning Li, Yong Yao, Quanming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14529 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution Zheng, Xiaohan Wei, Lanning Li, Yong Yao, Quanming Machine Learning 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. |
| title | Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.14529 |