Modeling Hypergraph Using Large Language Models

• URL: http://arxiv.org/abs/2510.11728v1
• Date: Thu, 09 Oct 2025 04:23:16 GMT
• Title: Modeling Hypergraph Using Large Language Models
• Authors: Bingqiao Gu, Jiale Zeng, Xingqin Qi, Dong Li,
• Abstract summary: Hypergraphs are used in higher-order clustering, hypergraph neural networks and computer vision.<n>Yet, compared to traditional pairwise graphs, real hypergraph datasets remain scarce in both scale and diversity.<n>We introduce HyperLLM, a novel LLM-driven hypergraph generator that simulates the formation and evolution of hypergraphs through a multi-agent collaboration.
• Score: 4.58199980642666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the advantages of hypergraphs in modeling high-order relationships in complex systems, they have been applied to higher-order clustering, hypergraph neural networks and computer vision. These applications rely heavily on access to high-quality, large-scale real-world hypergraph data. Yet, compared to traditional pairwise graphs, real hypergraph datasets remain scarce in both scale and diversity. This shortage significantly limits the development and evaluation of advanced hypergraph learning algorithms. Therefore, how to quickly generate large-scale hypergraphs that conform to the characteristics of real networks is a crucial task that has not received sufficient attention. Motivated by recent advances in large language models (LLMs), particularly their capabilities in semantic reasoning, structured generation, and simulating human behavior, we investigate whether LLMs can facilitate hypergraph generation from a fundamentally new perspective. We introduce HyperLLM, a novel LLM-driven hypergraph generator that simulates the formation and evolution of hypergraphs through a multi-agent collaboration. The framework integrates prompts and structural feedback mechanisms to ensure that the generated hypergraphs reflect key real-world patterns. Extensive experiments across diverse datasets demonstrate that HyperLLM achieves superior fidelity to structural and temporal hypergraph patterns, while requiring minimal statistical priors. Our findings suggest that LLM-based frameworks offer a promising new direction for hypergraph modeling.

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