Cited by 47Complex Network Analysis TechniquesTheoretical and Computational PhysicsStochastic processes and statistical mechanics

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Percolation in Self-Similar Networks

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Abstract

We provide a simple proof that graphs in a general class of self-similar networks have zero percolation threshold. The considered self-similar networks include random scale-free graphs with given expected node degrees and zero clustering, scale-free graphs with finite clustering and metric structure, growing scale-free networks, and many real networks. The proof and the derivation of the giant component size do not require the assumption that networks are treelike. Our results rely only on the observation that self-similar networks possess a hierarchy of nested subgraphs whose average degree grows with their depth in the hierarchy. We conjecture that this property is pivotal for percolation in networks.

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Crossref · 10.1103/physrevlett.106.048701

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Research works printed but not linked: 24

  1. 10.1016/S0764-4442(01)01991-7 · DOI
  2. 10.1007/s00023-003-0928-8 · DOI
  3. 10.1103/RevModPhys.80.1275 · DOI · Paper page
  4. 10.1002/rsa.3240060204 · DOI
  5. 10.1103/PhysRevLett.85.4626 · DOI
  6. 10.1016/j.physa.2004.05.020 · DOI
  7. 10.1103/PhysRevE.67.015101 · DOI
  8. 10.1103/PhysRevE.78.051105 · DOI
  9. 10.1103/PhysRevE.74.056114 · DOI
  10. 10.1103/PhysRevE.68.026121 · DOI
  11. 10.1103/PhysRevE.74.056115 · DOI
  12. 10.1103/PhysRevLett.97.088701 · DOI
  13. 10.1103/PhysRevLett.103.058701 · DOI
  14. 10.1103/PhysRevE.80.036107 · DOI
  15. 10.1103/PhysRevE.68.036112 · DOI
  16. 10.1103/PhysRevE.68.026112 · DOI
  17. 10.1103/PhysRevE.78.015101 · DOI
  18. 10.1140/epjb/e2004-00038-8 · DOI
  19. 10.1103/PhysRevLett.100.078701 · DOI
  20. 10.3934/nhm.2008.3.371 · DOI
  21. 10.1103/PhysRevE.66.036113 · DOI
  22. 10.1209/0295-5075/92/40013 · DOI
  23. 10.1103/PhysRevLett.85.4104 · DOI
  24. 10.1103/PhysRevE.79.036110 · DOI

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Metadata

Authors
Serrano, M. Á., Krioukov, D., & Boguñá, M.
Affiliations
  • Universitat de Barcelona (ES)
  • University of California San Diego (US)
Venue
Physical Review Letters
Published
2011-01-25
Volume
106
Issue
4
Pages
048701-048701
ISSN
  • 0031-9007 (ISSN-L)
  • 1079-7114
OpenAlex
W2066877881
Cited by
47
References
0
Citations by year
2026: 3 · 2025: 4 · 2024: 3 · 2023: 3 · 2022: 2 · 2021: 6 · 2019: 1 · 2018: 3 · 2017: 2 · 2016: 5 · 2015: 6 · 2014: 5 · 2013: 1 · 2012: 2
FWCI
2.20
Type
journal article
Language
en
Open access
Locations
  • Physical Review Letters (publishedVersion)
  • PubMed (publishedVersion)
  • RECERCAT (Consorci de Serveis Universitaris de Catalunya) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (submittedVersion, public-domain)
  • arXiv (Cornell University)
Funders
National Science Foundation
Topics
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Theoretical and Computational Physics
  • Physical Sciences › Mathematics › Mathematical Physics › Stochastic processes and statistical mechanics
Keywords
percolation threshold, self-similar networks, scale-free networks, network hierarchy, growing networks, clustering, real-world networks

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