The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Control Profiles of Complex Networks
Abstract
Real Network Control: Understanding how complex networks are controlled has implications for a variety of real-world networks, from traffic safety to transcriptional control. Ruths and Ruths (p. 1373 ; see the Perspective by Onnela ) have developed a theoretical framework for analyzing individual controls within networks based on numbers of sources and sinks for information flow. By this method, the number of controls required by a network can be predicted and direct comparisons for the basis for control across networks of differing size, structure, and function can be made. Although three broad classes of real networks were observed, current, established random models of networks were insufficient to model their control structures.
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The role of PFC networks in cognitive control and executive function
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Data based identification and prediction of nonlinear and complex dynamical systems
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Spectrum 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
Optimally controlling the human connectome: the role of network topology
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From maps to multi-dimensional network mechanisms of mental disorders
Braun, U., Schaefer, A., Betzel, R. F., Tost, H., Meyer-Lindenberg, A., & Bassett, D. S. (2018) · Neuron · doi:10.1016/j.neuron.2017.11.007 · Cited by 177
Metadata
- Authors
- Ruths, J., & Ruths, D.
- Venue
- Science
- Published
- 2014-03-21
- Volume
- 343
- Issue
- 6177
- Pages
- 1373-1376
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2078008084
- Cited by
- 391
- References
- 56
- FWCI
- 37.36
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
