The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Controlling Complex Networks: How Much Energy Is Needed?
Open accessAbstract
The outstanding problem of controlling complex networks is relevant to many areas of science and engineering, and has the potential to generate technological breakthroughs as well. We address the physically important issue of the energy required for achieving control by deriving and validating scaling laws for the lower and upper energy bounds. These bounds represent a reasonable estimate of the energy cost associated with control, and provide a step forward from the current research on controllability toward ultimate control of complex networked dynamical systems.
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Cites
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Newman, M. (2010) · doi:10.1093/acprof:oso/9780199206650.001.0001 · Cited by 6,595
Graph Theoretic Methods in Multiagent Networks
Mesbahi, M., & Egerstedt, M. (2010) · Princeton University Press eBooks · doi:10.1515/9781400835355 · Cited by 2,897
Dynamical Processes on Complex Networks
Barrat, A., Barthélemy, M., & Vespignani, A. (2008) · doi:10.1017/cbo9780511791383 · Cited by 2,704 · cited 1×
“On one hand, the couplings between nodes increase the complexity of collective behaviors, which stimulates much interest of modeling, analyzing, and predicting dynamical processes on complex networks [3].”
BackgroundBarabási, A.-L. (2012) · Nature Physics · doi:10.1038/nphys2188 · Cited by 502
Cited by
Controllability Metrics, Limitations and Algorithms for Complex Networks
Pasqualetti, F., Zampieri, S., & Bullo, F. (2014) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2014.2310254 · Cited by 594
Data based identification and prediction of nonlinear and complex dynamical systems
Wang, W.-X., Lai, Y.-C., & Grebogi, C. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.06.004 · Cited by 299
Self-citationSpectrum of controlling and observing complex networks
Yan, G., Tsekenis, G., Barzel, B., Slotine, J.-J., Liu, Y.-Y., & Barabási, A.-L. (2015) · Nature Physics · doi:10.1038/nphys3422 · Cited by 279
Self-citationOptimally controlling the human connectome: the role of network topology
Betzel, R. F., Gu, S., Medaglia, J. D., Pasqualetti, F., & Bassett, D. S. (2016) · Scientific Reports · doi:10.1038/srep30770 · Cited by 247
Optimal trajectories of brain state transitions
Gu, S., Betzel, R. F., Mattar, M. G., Cieslak, M., Delio, P. R., Grafton, S. T., Pasqualetti, F., & Bassett, D. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.003 · Cited by 183
Metadata
- Authors
- Yan, G., Ren, J., Lai, Y.-C., Lai, C.-H., & Li, B.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2012-05-23
- Volume
- 108
- Issue
- 21
- Pages
- 218703-218703
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2104109956
- Cited by
- 413
- References
- 5
- FWCI
- 23.11
- Type
- journal article
- Language
- en
- Open access
- green
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- Yan, G., Ren, J., Lai, Y.-C., Lai, C.-H., & Li, B. (2012). Controlling Complex Networks: How Much Energy Is Needed? Physical Review Letters. https://doi.org/10.1103/physrevlett.108.218703
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