The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Critical effect of dependency groups on the function of networks
Open accessAbstract
Current network models assume one type of links to define the relations between the network entities. However, many real networks can only be correctly described using two different types of relations. Connectivity links that enable the nodes to function cooperatively as a network and dependency links that bind the failure of one network element to the failure of other network elements. Here we present an analytical framework for studying the robustness of networks that include both connectivity and dependency links. We show that a synergy exists between the failure of connectivity and dependency links that leads to an iterative process of cascading failures that has a devastating effect on the network stability. We present exact analytical results for the dramatic change in the network behavior when introducing dependency links. For a high density of dependency links, the network disintegrates in a form of a first-order phase transition, whereas for a low density of dependency links, the network disintegrates in a second-order transition. Moreover, opposed to networks containing only connectivity links where a broader degree distribution results in a more robust network, when both types of links are present a broad degree distribution leads to higher vulnerability.
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
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Identifying, understanding, and analyzing critical infrastructure interdependencies
(2001) · IEEE Control Systems · doi:10.1109/37.969131 · Cited by 2,851
Dynamical Processes on Complex Networks
Barrat, A., Barthélemy, M., & Vespignani, A. (2008) · doi:10.1017/cbo9780511791383 · Cited by 2,704 · cited 1×
“[6] Barrat A, Barthelemy M, Vespignani A.”
Cascade-based attacks on complex networks
Motter, A. E., & Lai, Y.-C. (2002) · Physical Review E · doi:10.1103/physreve.66.065102 · Cited by 1,929
Dorogovtsev, S. N., & Mendes, J. F. F. (2003) · doi:10.1093/acprof:oso/9780198515906.001.0001 · Cited by 1,563
Cascade Control and Defense in Complex Networks
Motter, A. E. (2004) · Physical Review Letters · doi:10.1103/physrevlett.93.098701 · Cited by 776
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
The structure and dynamics of multilayer networks
Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., & Zanin, M. (2014) · Physics Reports · doi:10.1016/j.physrep.2014.07.001 · Cited by 3,125
Robustness of interdependent networks under targeted attack
Huang, X., Gao, J., Buldyrev, S. V., Havlin, S., & Stanley, H. E. (2011) · Physical Review E · doi:10.1103/physreve.83.065101 · Cited by 527
Self-citationEvolution of Cooperation in Multiplex Networks
Gómez-Gardeñes, J., Reinares, I., Arenas, A., & Floría, L. M. (2012) · Scientific Reports · doi:10.1038/srep00620 · Cited by 462
Explosive transitions in complex networks’ structure and dynamics: Percolation and synchronization
Boccaletti, S., Almendral, J. A., Guan, S., Leyva, I., Liu, Z., Sendiña-Nadal, I., Wang, Z., & Zou, Y. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.10.004 · Cited by 363
Metadata
- Authors
- Parshani, R., Buldyrev, S. V., & Havlin, S.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2010-12-29
- Volume
- 108
- Issue
- 3
- Pages
- 1007-1010
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2030969083
- Cited by
- 303
- References
- 6
- FWCI
- 14.32
- 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: Critical effect of dependency groups on the function of networks. https://app.cochat.ai/papers/10.1073/pnas.1008404108
- The paper
- Parshani, R., Buldyrev, S. V., & Havlin, S. (2011). Critical effect of dependency groups on the function of networks. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1008404108
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.