用语言描述网络结构，让大模型能理解复杂系统的拓扑关系。
Language-encoded network topology enables large language models to reason about complex networks
- 将网络拓扑转化为可读的结构角色语言，如枢纽、社区核心等。
- 在20个跨领域网络上提升大模型推理准确率最高达26个百分点。
- 特别适合生物网络等密集有结构的系统，结果可解释性强。
网络描述生物学等领域的系统，如蛋白质相互作用、社交关系、电网和引用记录。理解这些系统需把握其结构：哪些元素是中心，哪些连接桥接不同社区，以及移除元素后如何变化。尽管大语言模型（LLMs）擅长自然语言，但当网络以边列表、句子或表格形式给出时，它们难以应对，因结构含义需自行推断。本文提出BioGlyph，将网络拓扑编译为可解释且可迁移的结构角色语言。BioGlyph结合图分割与结构度量，识别枢纽、社区核心和跨社区连接者等角色，并通过固定规则转化为通用词汇。该表示通过结构角色、支持证据和语义后果描述每个节点，不改变网络或语言模型本身。在涵盖五个领域的20个网络中，BioGlyph显著提升开放大模型对结构推理问题的回答能力，比基于边、数值和学习的表示高出最多26个百分点。消融实验表明，提升源于以语义可解释方式显式编码结构角色。在密集、有社区结构的网络中增益更明显，而在拓扑信息易从文本推断的稀疏网络中减弱。在酿酒酵母蛋白互作网络中，发现跨社区连接者富集于必需基因，而外围蛋白则贫乏。BioGlyph为语言模型与科学家提供了可解释的网络结构推理工具。
原文摘要 · Abstract (English)
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.
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