The network of interactions among fluid elements and coherent structures gives rise to the incredibly rich dynamics of vortical flows. These interactions can be described with the use of mathematical tools from the emerging field of network science, which leverages graph theory, dynamical systems theory, data science, and control theory. The blending of network science and fluid mechanics facilitates the extraction of the key interactions and communities in terms of vortical elements, modal structures, and particle trajectories. Phase-space techniques and time-delay embedding enable a network-based analysis of time-series measurements in terms of visibility, recurrence, and cluster transitions. Equipped with the knowledge of interactions and communities, the network-theoretic approach enables the analysis, modeling, and control of fluid flows, with a particular emphasis on interactive dynamics. In this article, we provide a brief introduction to network science and an overview of the progress on network-based strategies to study the complex dynamics of fluid flows. Case studies are surveyed to highlight the utility of network-based techniques to tackle a range of problems from fluid mechanics. Towards the end of the paper, we offer an outlook on network-inspired approaches.

流体元素和连贯结构之间的相互作用网络产生了令人难以置信的丰富的涡流动力学。这些相互作用可以使用新兴网络科学领域的数学工具来描述，该领域利用图论、动态系统理论、数据科学和控制理论。网络科学和流体力学的融合有助于提取涡旋元素、模态结构和粒子轨迹方面的关键相互作用和群落。相空间技术和时间延迟嵌入使基于网络的时间序列测量在可见性、重复性和集群转换方面的分析成为可能。网络理论方法具备相互作用和社区的知识，能够对流体流动进行分析、建模和控制，特别强调互动动态。在本文中，我们简要介绍了网络科学，并概述了基于网络的策略研究流体流动复杂动力学的进展。对案例研究进行了调查，以突出基于网络的技术在解决流体力学中的一系列问题方面的效用。在本文的最后，我们对受网络启发的方法进行了展望。