大規模で複雑なネットワークの制御という課題に応える新手法 New method addresses challenge of controlling large complex networks
In their study, the researchers demonstrate the effectiveness of their method through four concrete examples, including the stability control of the Eastern U.S. power grid.
局所的なネットワークの普及とスケーラブルな制御 Prevalence and scalable control of localized networks
Chao Duan , Takashi Nishikawa and Adilson E. Motter
Proceedings of the National Academy of Sciences Published:August 5, 2022
The control of large-scale networks is a pressing problem of relevance to numerous natural and engineered systems. Despite recent advances in network and control science, there has been a lack of fundamental understanding about the network properties that can enable effective and efficient control of such systems. Here, we demonstrate that network locality, which we show to be a rather common property, can dramatically improve our ability to control large-scale networks. In particular, we demonstrate that locality can be exploited to substantially simplify the task of controlling nonlinear networks for desirable dynamical performance while minimizing the control effort. Our theory and algorithms provide a unified framework and show that local computation and communication suffice to achieve near-optimal control outcomes.
The ability to control network dynamics is essential for ensuring desirable functionality of many technological, biological, and social systems. Such systems often consist of a large number of network elements, and controlling large-scale networks remains challenging because the computation and communication requirements increase prohibitively fast with network size. Here, we introduce a notion of network locality that can be exploited to make the control of networks scalable, even when the dynamics are nonlinear. We show that network locality is captured by an information metric and is almost universally observed across real and model networks. In localized networks, the optimal control actions and system responses are both shown to be necessarily concentrated in small neighborhoods induced by the information metric. This allows us to develop localized algorithms for determining network controllability and optimizing the placement of driver nodes. This also allows us to develop a localized algorithm for designing local feedback controllers that approach the performance of the corresponding best global controllers, while incurring a computational cost orders-of-magnitude lower. We validate the locality, performance, and efficiency of the algorithms in Kuramoto oscillator networks, as well as three large empirical networks: synchronization dynamics in the Eastern US power grid, epidemic spreading mediated by the global air-transportation network, and Alzheimer’s disease dynamics in a human brain network. Taken together, our results establish that large networks can be controlled with computation and communication costs comparable to those for small networks.