The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Network Controllability Is Determined by the Density of Low In-Degree and Out-Degree Nodes
Open accessAbstract
The problem of controllability of the dynamical state of a network is central in network theory and has wide applications ranging from network medicine to financial markets. The driver nodes of the network are the nodes that can bring the network to the desired dynamical state if an external signal is applied to them. Using the framework of structural controllability, here, we show that the density of nodes with in degree and out degree equal to one and two determines the number of driver nodes in the network. Moreover, we show that random networks with minimum in degree and out degree greater than two, are always fully controllable by an infinitesimal fraction of driver nodes, regardless of the other properties of the degree distribution. Finally, based on these results, we propose an algorithm to improve the controllability of networks.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Functional Network Organization of the Human Brain
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.006 · Cited by 4,124
Dynamical Processes on Complex Networks
Barrat, A., Barthélemy, M., & Vespignani, A. (2008) · doi:10.1017/cbo9780511791383 · Cited by 2,704
GABAergic Hub Neurons Orchestrate Synchrony in Developing Hippocampal Networks
Bonifazi, P., Goldin, M., Picardo, M. A., Jorquera, I., Cattani, A., Bianconi, G., Represa, A., Ben-Ari, Y., & Cossart, R. (2009) · Science · doi:10.1126/science.1175509 · Cited by 695
Self-citation
Cited by
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
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
Interdisciplinary and physics challenges of network theory
Bianconi, G. (2015) · EPL (Europhysics Letters) · doi:10.1209/0295-5075/111/56001 · Cited by 126
Self-citation
Metadata
- Authors
- Menichetti, G., Dall’Asta, L., & Bianconi, G.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2014-08-13
- Volume
- 113
- Issue
- 7
- Pages
- 078701-078701
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2080844985
- Cited by
- 160
- References
- 4
- FWCI
- 13.62
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Network Controllability Is Determined by the Density of Low In-Degree and Out-Degree Nodes. https://app.cochat.ai/papers/10.1103/physrevlett.113.078701
- The paper
- Menichetti, G., Dall’Asta, L., & Bianconi, G. (2014). Network Controllability Is Determined by the Density of Low In-Degree and Out-Degree Nodes. Physical Review Letters. https://doi.org/10.1103/physrevlett.113.078701
Data sources
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.
