IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v351y2005i2p680-686.html
   My bibliography  Save this article

A star-based model for the eigenvalue power law of Internet graphs

Author

Listed:
  • Comellas, Francesc
  • Gago, Silvia

Abstract

Using a simple deterministic model for the Internet graph we show that the eigenvalue power-law distribution for its adjacency matrix is a direct consequence of the degree distribution and that the graph must contain many star subgraphs.

Suggested Citation

  • Comellas, Francesc & Gago, Silvia, 2005. "A star-based model for the eigenvalue power law of Internet graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 351(2), pages 680-686.
  • Handle: RePEc:eee:phsmap:v:351:y:2005:i:2:p:680-686
    DOI: 10.1016/j.physa.2005.01.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437105000336
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2005.01.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Barabási, Albert-László & Ravasz, Erzsébet & Vicsek, Tamás, 2001. "Deterministic scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(3), pages 559-564.
    2. Comellas, Francesc & Sampels, Michael, 2002. "Deterministic small-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 309(1), pages 231-235.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Zhongzhi & Rong, Lili & Comellas, Francesc, 2006. "High-dimensional random Apollonian networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 610-618.
    2. Zhong, Xiang & Liu, Jiajun & Gao, Yong & Wu, Lun, 2017. "Analysis of co-occurrence toponyms in web pages based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 462-475.
    3. Blagus, Neli & Šubelj, Lovro & Bajec, Marko, 2012. "Self-similar scaling of density in complex real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2794-2802.
    4. Razdan, Ashok, 2013. "Networks in extensive air showers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 982-986.
    5. Gao, Yan & Liu, Gengyuan & Casazza, Marco & Hao, Yan & Zhang, Yan & Giannetti, Biagio F., 2018. "Economy-pollution nexus model of cities at river basin scale based on multi-agent simulation: A conceptual framework," Ecological Modelling, Elsevier, vol. 379(C), pages 22-38.
    6. Blasi, Monica Francesca & Casorelli, Ida & Colosimo, Alfredo & Blasi, Francesco Simone & Bignami, Margherita & Giuliani, Alessandro, 2005. "A recursive network approach can identify constitutive regulatory circuits in gene expression data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 349-370.
    7. Katz, J. Sylvan, 2006. "Indicators for complex innovation systems," Research Policy, Elsevier, vol. 35(7), pages 893-909, September.
    8. Hollingshad, Nicholas W. & Turalska, Malgorzata & Allegrini, Paolo & West, Bruce J. & Grigolini, Paolo, 2012. "A new measure of network efficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1894-1899.
    9. Zhang, Yue & Huang, Ning & Xing, Liudong, 2016. "A novel flux-fluctuation law for network with self-similar traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 299-310.
    10. Sun, Lina & Huang, Ning & Li, Ruiying & Bai, Yanan, 2019. "A new fractal reliability model for networks with node fractal growth and no-loop," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 699-707.
    11. Lu, Zhe-Ming & Guo, Shi-Ze, 2012. "A small-world network derived from the deterministic uniform recursive tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 87-92.
    12. Yinhu Zhai & Jia-Bao Liu & Shaohui Wang, 2017. "Structure Properties of Koch Networks Based on Networks Dynamical Systems," Complexity, Hindawi, vol. 2017, pages 1-7, March.
    13. Farkas, I & Derényi, I & Jeong, H & Néda, Z & Oltvai, Z.N & Ravasz, E & Schubert, A & Barabási, A.-L & Vicsek, T, 2002. "Networks in life: scaling properties and eigenvalue spectra," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 314(1), pages 25-34.
    14. Tachimori, Yutaka & Iwanaga, Hiroaki & Tahara, Takashi, 2013. "The networks from medical knowledge and clinical practice have small-world, scale-free, and hierarchical features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6084-6089.
    15. Guillaume, Jean-Loup & Latapy, Matthieu, 2006. "Bipartite graphs as models of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 795-813.
    16. Chen, Mu & Yu, Boming & Xu, Peng & Chen, Jun, 2007. "A new deterministic complex network model with hierarchical structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(2), pages 707-717.
    17. Zheng, Xiaolong & Zeng, Daniel & Li, Huiqian & Wang, Feiyue, 2008. "Analyzing open-source software systems as complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6190-6200.
    18. Park, Ji Hwan & Chang, Woojin & Song, Jae Wook, 2020. "Link prediction in the Granger causality network of the global currency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    19. Ye, Dandan & Dai, Meifeng & Sun, Yu & Su, Weiyi, 2017. "Average weighted receiving time on the non-homogeneous double-weighted fractal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 390-402.
    20. Kumar, Viney & Bhattacharyya, Samit, 2023. "Nonlinear effect of sentiments and opinion sharing on vaccination decision in face of an outbreak: A multiplex network approach," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:351:y:2005:i:2:p:680-686. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.