ArticleNature communications2021
Data-driven control of complex networks.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
15 citing papers in PubMed.
- Control of cortical population activity with patterned microstimulation.bioRxiv : the preprint server for biology · 2026Article
- Data-Driven Pattern Formation in Oscillator Networks Using Partial Observations.Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control · 2025Article
- Tumor microenvironment governs the prognostic landscape of immunotherapy for head and neck squamous cell carcinoma: A computational model-guided analysis.PLoS computational biology · 2025Article
- MetaCity: Data-driven sustainable development of complex cities.Innovation (Cambridge (Mass.)) · 2025Review
- The neuron as a direct data-driven controller.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Cell reprogramming design by transfer learning of functional transcriptional networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Modelling and Controlling System Dynamics of the Brain: An Intersection of Machine Learning and Control Theory.Advances in neurobiology · 2024Review
- Full Bayesian identification of linear dynamic systems using stable kernels.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Data-Driven Control of Neuronal Networks with Population-Level Measurement.Research square · 2023Article
- Controlling the human microbiome.Cell systems · 2023Review
- Exact Decomposition of Optimal Control Problems via Simultaneous Block Diagonalization of Matrices.IEEE open journal of control systems · 2023Article
- Conserved Control Path in Multilayer Networks.Entropy (Basel, Switzerland) · 2022Article
- Discovering spatial-temporal patterns via complex networks in investigating COVID-19 pandemic in the United States.Sustainable cities and society · 2022Article
- Functional network motifs defined through integration of protein-protein and genetic interactions.PeerJ · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Our ability to manipulate the behavior of complex networks depends on the design of efficient control algorithms and, critically, on the availability of an accurate and tractable model of the network dynamics. While the design of control algorithms for network systems has seen notable advances in the past few years, knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. In this paper we overcome this limitation, and develop a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics. Our optimal controls are constructed using a finite set of data, where the unknown network is stimulated with arbitrary and possibly random inputs. Although our controls are provably correct for networks with linear dynamics, we also characterize their performance against noisy data and in the presence of nonlinear dynamics, as they arise in power grid and brain networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.