The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Emergence of bimodality in controlling complex networks
Open accessAbstract
Our ability to control complex systems is a fundamental challenge of contemporary science. Recently introduced tools to identify the driver nodes, nodes through which we can achieve full control, predict the existence of multiple control configurations, prompting us to classify each node in a network based on their role in control. Accordingly a node is critical, intermittent or redundant if it acts as a driver node in all, some or none of the control configurations. Here we develop an analytical framework to identify the category of each node, leading to the discovery of two distinct control modes in complex systems: centralized versus distributed control. We predict the control mode for an arbitrary network and show that one can alter it through small structural perturbations. The uncovered bimodality has implications from network security to organizational research and offers new insights into the dynamics and control of complex systems. The control of a complex network can be achieved by different combinations of relatively few driver nodes. Tao Jia and colleagues show that this can lead to two distinct control modes—centralized or distributed—that determine the number of nodes that can act as driver node.
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
Network motifs: simple building blocks of complex networks.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002) · Science · doi:10.1126/science.298.5594.824 · Cited by 7,368
The structure of the nervous system of the nematode Caenorhabditis elegans
White, J. G., Southgate, E., Thomson, J. N., & Brenner, S. (1986) · Philosophical Transactions of the Royal Society of London. B, Biological Sciences · doi:10.1098/rstb.1986.0056 · Cited by 5,589
Hierarchical Organization of Modularity in Metabolic Networks
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A.-L. (2002) · Science · doi:10.1126/science.1073374 · Cited by 4,224
Limits of Predictability in Human Mobility
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234
Self-citationThe political blogosphere and the 2004 U.S. election: divided they blog
Adamic, L. A., & Glance, N. (2005) · Proceedings of the 3rd international workshop on Link discovery · doi:10.1145/1134271.1134277 · Cited by 2,619
Barabási, A.-L., & Bonabeau, E. (2003) · Scientific American · doi:10.1038/scientificamerican0503-60 · Cited by 2,494
Self-citationGraphs over time: densification laws, shrinking diameters and possible explanations
Leskovec, J., Kleinberg, J., & Faloutsos, C. (2005) · Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining · doi:10.1145/1081870.1081893 · Cited by 2,281
Hierarchy Measure for Complex Networks
Mones, E., Vicsek, L., & Vicsek, T. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033799 · Cited by 259
Cited by
Control Profiles of Complex Networks
Ruths, J., & Ruths, D. (2014) · Science · doi:10.1126/science.1242063 · Cited by 391
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
Self-citationVinayagam, A., Gibson, T. E., Lee, H.-J., Yilmazel, B., Roesel, C., Hu, Y., Kwon, Y., Sharma, A., Liu, Y.-Y., Perrimon, N., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1603992113 · Cited by 257
Self-citation
Metadata
- Authors
- Jia, T., Liu, Y.-Y., Csóka, E., Pósfai, M., Slotine, J.-J., & Barabási, A.-L.
- Venue
- Nature Communications
- Published
- 2013-06-18
- Volume
- 4
- Issue
- 1
- Pages
- 2002-2002
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2082019892
- Cited by
- 220
- References
- 8
- FWCI
- 19.61
- Type
- journal article
- Language
- en
- Open access
- bronze
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
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.