AI 中文总结
研究复杂网络有向结构规律，通过有向无环图性质框架对107个网络实证评估，发现不同系统分解为四种通用结构原型及宏观无环性普遍，该框架为理解复杂系统有向结构提供统一视角。
AI 中文摘要
有向网络出现在生物、社会、信息和工程系统中，但大多数分析将方向性视为二元属性：网络要么是有向无环图（DAG），要么不是。这种二元分类掩盖了实际系统中丰富的层次、循环和模块化结构。本文中，我们实证评估了有向无环图性质框架，这是一种四分量度量，用于量化从十二个结构多样的领域抽取的107个网络语料库中的无环性、流对齐、循环局部性和路径复杂性。结果揭示了意想不到的跨领域收敛：不同系统分解为四种通用结构原型。我们发现宏观无环性即使在富含反馈的系统中也很普遍，神经连接组和抽象信息网络等不同领域经常收敛于相同的拓扑约束。这些发现表明有向无环图性质为理解复杂系统中隐藏的有向结构规律提供了一个统一、可解释且与领域无关的视角。
英文摘要
Directed networks arise across biological, social, informational, and engineered systems, yet most analyses treat directedness as a binary property: a network is either a directed acyclic graph (DAG) or it is not. This binary classification obscures the rich spectrum of hierarchical, recurrent, and modular structure present in real systems. In this paper, we empirically evaluate the DAG-ness framework, a four-component measure that quantifies acyclicity, flow alignment, cyclic locality, and pathway complexity across a corpus of 107 networks drawn from twelve structurally diverse domains. Rather than aligning with traditional disciplinary boundaries, our results reveal unexpected cross-domain convergence: diverse systems resolve into four universal structural archetypes. We find that macroscopic acyclicity is pervasive even in feedback-rich systems, and that domains as disparate as neural connectomes and abstract informational networks frequently converge on identical topological constraints. These findings demonstrate that DAG-ness provides a unified, interpretable, and domain-agnostic lens for understanding the hidden laws of directed structure in complex systems.
