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About this paper
Ekta Gujral, Apurva Sinha
A new framework, CommLLM, leverages GPT-4o and a graph-to-text conversion method to perform community detection in social network graphs. The system achieves higher accuracy compared to other LLM baselines and successfully processes medium-sized graphs, demonstrating the critical role of prompt engineering in its performance.
Key insights
- Large Language Models can effectively perform community detection when graph topology is precisely converted into a textual format that preserves network structure.
- Meticulous prompt engineering, including clear task definitions and strict output formatting, is critical for unlocking the full reasoning potential of LLMs in graph-based tasks.
- While LLMs face inherent scalability limitations for very large graphs due to context window constraints, tailored approaches can extend their applicability to larger medium-sized networks.
Summary by alphaXiv.
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