Language-encoded network topology enables large language models to reason about complex networks
作者: Ucchwas Talukder Utsha, Sakib Mostafa, James Zou, Md Tauhidul Islam
分类: cs.LG
发布日期: 2026-09-03
💡 一句话要点
提出BioGlyph以解决大语言模型对复杂网络推理的挑战
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 网络推理 大语言模型 结构角色 BioGlyph 图划分 生物信息学 社交网络分析
📋 核心要点
- 现有的大语言模型在处理复杂网络时，无法有效推理其结构特征，导致推理能力不足。
- 论文提出BioGlyph，通过将网络拓扑转化为结构角色的可解释语言，帮助模型理解网络结构。
- 在二十个不同领域的网络实验中，BioGlyph使模型的结构推理准确率提高了最多26个百分点。
📝 摘要（中文）
网络在生物学等多个领域中描述系统，包括蛋白质相互作用、社会关系、电力网络和引用记录。推理这些系统需要理解其结构，然而现有的大语言模型在处理边列表、句子或测量表时表现不佳，因为其结构意义需被推断。本文提出BioGlyph，通过将网络拓扑编译成可解释和可转移的结构角色语言，显著提升了开放式大语言模型在结构推理问题上的表现。BioGlyph结合图划分和结构测量，识别中心节点、社区核心和跨社区连接器等角色，并将其翻译为通用词汇。实验表明，BioGlyph在二十个网络中显著提高了模型的准确性，尤其在密集的社区结构网络中效果更为明显。
🔬 方法详解
问题定义：本文旨在解决大语言模型在处理复杂网络时的推理能力不足，现有方法无法有效理解网络的结构特征，导致推理结果不准确。
核心思路：BioGlyph通过将网络拓扑编译成可解释的结构角色语言，使得大语言模型能够直接理解网络中各个元素的角色，从而提升推理能力。
技术框架：BioGlyph的整体架构包括图划分和结构测量两个主要模块，首先识别网络中的不同角色，然后将这些角色翻译为通用的语言表示。
关键创新：BioGlyph的核心创新在于将网络元素的结构角色以语义可解释的形式编码，这一方法显著优于传统的边基、数值和学习表示方法。
关键设计：在设计中，BioGlyph采用固定规则将角色翻译为通用词汇，并通过结构测量来识别角色，如中心节点和社区核心等。
🖼️ 关键图片
📊 实验亮点
实验结果显示，BioGlyph在二十个网络的结构推理任务中，较传统方法提高了最多26个百分点的准确率。尤其在密集的社区结构网络中，BioGlyph的优势更加明显，验证了其在复杂网络分析中的有效性。
🎯 应用场景
BioGlyph的研究成果在生物学、社交网络分析和电力系统等多个领域具有广泛的应用潜力。通过提供可解释的网络结构表示，科学家和研究人员能够更好地理解复杂系统的动态变化和结构特征，从而推动相关领域的研究进展。
📄 摘要（原文）
Networks describe systems in biology and beyond, from protein interactions and social relationships to power grids and citation records. Reasoning about such systems requires understanding their structure: which elements are central, which connections bridge separate communities, and how it changes when elements are removed. Although large language models (LLMs) excel at natural language, they struggle with such questions when networks are given as edge lists, sentences or measurement tables, because their structural meaning must be inferred. Here we introduce BioGlyph, which compiles network topology into an interpretable and transferable language of structural roles. BioGlyph combines graph partitioning and structural measurements to identify roles such as hubs, community cores and cross-community connectors, and fixed rules to translate them into a universal vocabulary. The representation describes each element through its structural role, supporting evidence and semantic consequences, leaving both the network and the LLM unchanged. Across twenty networks spanning five domains, BioGlyph substantially improves open LLMs' ability to answer structural reasoning questions, outperforming edge-based, numerical and learned representations by up to 26 percentage points in system accuracy. Ablations show that the gain comes from explicitly encoding structural roles in semantically interpretable terms. The gain is more prominent in dense, community-structured networks and diminishes in sparse networks whose topology is more readily inferred from text. In a budding-yeast protein-interaction network, BioGlyph exposes biological organization: cross-community connectors are enriched for essential genes, whereas peripheral proteins are depleted. BioGlyph thus provides an interpretable representation for both language models and scientists to reason about network structure.