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Nodal Dynamics, Not Degree Distributions, Determine the Structural Controllability of Complex Networks
Open accessAbstract
Structural controllability has been proposed as an analytical framework for making predictions regarding the control of complex networks across myriad disciplines in the physical and life sciences (Liu et al., Nature:473(7346):167-173, 2011). Although the integration of control theory and network analysis is important, we argue that the application of the structural controllability framework to most if not all real-world networks leads to the conclusion that a single control input, applied to the power dominating set, is all that is needed for structural controllability. This result is consistent with the well-known fact that controllability and its dual observability are generic properties of systems. We argue that more important than issues of structural controllability are the questions of whether a system is almost uncontrollable, whether it is almost unobservable, and whether it possesses almost pole-zero cancellations.
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Cites
Strogatz, S. H. (2001) · Nature · doi:10.1038/35065725 · Cited by 8,302
Principal component analysis in linear systems: Controllability, observability, and model reduction
Moore, B. (1981) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1981.1102568 · Cited by 5,282
Linear Optimal Control Systems
Kwakernaak, H., Sivan, R., & Tyreus, B. N. D. (1974) · Journal of Dynamic Systems, Measurement, and Control · doi:10.1115/1.3426828 · Cited by 4,976
Controllability of complex networks
Liu, Y.-Y., Slotine, J.-J., & Barabási, A.-L. (2011) · Nature · doi:10.1038/nature10011 · Cited by 3,215
Rich, V. (1989) · Nature · doi:10.1038/338005b0 · Cited by 3,000
Clustering and preferential attachment in growing networks
Newman, M. E. J. (2001) · Physical Review E · doi:10.1103/physreve.64.025102 · Cited by 2,093
Lin, C.-T. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100557 · Cited by 1,397
Principles of rhythmic motor pattern generation
Marder, E., & Calabrese, R. L. (1996) · Physiological Reviews · doi:10.1152/physrev.1996.76.3.687 · Cited by 1,276
Pinning control of scale-free dynamical networks
Wang, X. F., & Chen, G. (2002) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(02)00772-0 · Cited by 1,008
Analytical Note on Certain Rhythmic Relations in Organic Systems
Lotka, A. J. (1920) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.6.7.410 · Cited by 936
Cited by
Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014) · Journal of Complex Networks · doi:10.1093/comnet/cnu016 · Cited by 3,235
Structure and dynamics of molecular networks: A novel paradigm of drug discovery
Csermely, P., Korcsmáros, T., Kiss, H. J. M., London, G., & Nussinov, R. (2013) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2013.01.016 · Cited by 892
Closed-Loop Turbulence Control: Progress and Challenges
Brunton, S. L., & Noack, B. R. (2015) · Applied Mechanics Reviews · doi:10.1115/1.4031175 · Cited by 623
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
Control Profiles of Complex Networks
Ruths, J., & Ruths, D. (2014) · Science · doi:10.1126/science.1242063 · Cited by 391
Metadata
- Authors
- Cowan, N. J., Chastain, E. J., Vilhena, D. A., Freudenberg, J. S., & Bergstrom, C. T.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-06-22
- Volume
- 7
- Issue
- 6
- Pages
- e38398
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W1972577981
- Cited by
- 273
- References
- 20
- FWCI
- 18.73
- Type
- journal article
- Language
- en
- Open access
- gold
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- Cowan, N. J., Chastain, E. J., Vilhena, D. A., Freudenberg, J. S., & Bergstrom, C. T. (2012). Nodal Dynamics, Not Degree Distributions, Determine the Structural Controllability of Complex Networks. PLoS ONE. https://doi.org/10.1371/journal.pone.0038398
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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