使用大语言模型建模超图
Modeling Hypergraph Using Large Language Models
AI总结：
针对真实超图数据稀缺问题，提出LLM驱动的HyperLLM生成器，通过多智能体协作模拟超图形成演化，以最小统计先验实现高保真结构与时序模式生成。
摘要：
由于超图在建模复杂系统中的高阶关系方面具有优势，它们已被应用于高阶聚类、超图神经网络和计算机视觉。这些应用在很大程度上依赖于获取高质量、大规模的真实世界超图数据。然而，与传统的成对图相比，真实的超图数据集在规模和多样性方面仍然稀缺。这种短缺极大地限制了先进超图学习算法的开发和评估。因此，如何快速生成符合真实网络特征的大规模超图，是一个尚未得到足够重视的关键任务。受大语言模型（LLMs）近期进展的启发，特别是其在语义推理、结构化生成和模拟人类行为方面的能力，我们研究LLMs是否能够从一个全新的角度促进超图生成。我们提出了HyperLLM，一种新颖的由LLM驱动的超图生成器，通过多智能体协作来模拟超图的形成和演化。该框架整合了提示和结构反馈机制，以确保生成的超图反映关键的真实世界模式。跨多个数据集的广泛实验表明，HyperLLM在结构和时间超图模式方面实现了卓越的保真度，同时仅需最少的统计先验。我们的研究结果表明，基于LLM的框架为超图建模提供了一个有前景的新方向。
英文摘要：
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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