The increasing complexity and interconnectedness of systems across various fields have led to a growing interest in studying complex networks, particularly Scale-Free (SF) networks, which best model real-world systems. This paper investigates the influence of clustering on the observability and controllability of complex SF networks, framing these characteristics in the context of structured systems theory. In this paper, we show that densely clustered networks require fewer driver and observer nodes due to better information propagation within clusters. This relationship is of interest for optimizing network design in applications such as social networks and intelligent transportation systems. We first quantify the network observability/controllability requirements, and then, through Monte-Carlo simulations and different case studies, we show how clustering affects these metrics. Our findings offer practical insights into reducing control and observer nodes for sensor/actuator placement, particularly in resource-constrained setups. This work contributes to the understanding of network observability/controllability and presents techniques for improving these features through alterations in network structure and clustering.

翻译：随着各领域系统日益复杂且相互关联，对复杂网络，特别是最能模拟现实系统的无标度网络的研究兴趣日益增长。本文研究了聚类对复杂无标度网络可观测性与可控性的影响，并将这些特性置于结构化系统理论的框架下进行分析。本文表明，由于簇内信息传播更高效，密集聚类的网络需要更少的驱动节点和观测节点。这一关系对于优化社交网络和智能交通系统等应用中的网络设计具有重要意义。我们首先量化了网络可观测性/可控性的要求，随后通过蒙特卡洛模拟和多个案例研究，展示了聚类如何影响这些指标。我们的研究结果为传感器/执行器布置中减少控制节点和观测节点，特别是在资源受限的场景下，提供了实用的见解。这项工作深化了对网络可观测性/可控性的理解，并提出了通过改变网络结构和聚类来改善这些特性的技术。