有向网络广泛存在于生物、社会、信息及工程系统中，但多数分析将有向性视为二元属性：一个网络要么是有向无环图（DAG），要么不是。这种二元分类掩盖了真实系统中所蕴含的丰富层次性、循环性与模块化结构。本文实证评估了DAG性框架——一种包含四个分量的度量方法，用于量化107个来自十二个结构多样性领域的网络在无环性、流向一致性、循环局部性及路径复杂性四个维度上的特征。我们的结果并未沿袭传统学科边界，而是揭示出跨领域意外收敛现象：不同系统普遍归为四种普适性结构原型。我们发现，宏观尺度上的无环性即使在富含反馈的系统中亦普遍存在；而神经连接组与抽象信息网络等迥异领域，也常受制于相同的拓扑约束。这些发现表明，DAG性为理解复杂系统中有向结构的潜在规律提供了一种统一、可解释且领域无关的分析视角。

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.