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. Comellas, Francesc & Sampels, Michael, 2002. "Deterministic small-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 309(1), pages 231-235.
    2. 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.
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. Dai, Meifeng & Shao, Shuxiang & Su, Weiyi & Xi, Lifeng & Sun, Yanqiu, 2017. "The modified box dimension and average weighted receiving time of the weighted hierarchical graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 475(C), pages 46-58.
    10. Sylvan Katz, 2005. "Indicators for Complex Innovation Systems," SPRU Working Paper Series 134, SPRU - Science Policy Research Unit, University of Sussex Business School.
    11. Dangalchev, Chavdar, 2004. "Generation models for scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(3), pages 659-671.
    12. Chen, Jin & Dai, Meifeng & Wen, Zhixiong & Xi, Lifeng, 2014. "Trapping on modular scale-free and small-world networks with multiple hubs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 542-552.
    13. Zeng, Cheng & Xue, Yumei & Huang, Yuke, 2021. "Fractal networks with Sturmian structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    14. Komjáthy, Júlia & Simon, Károly, 2011. "Generating hierarchial scale-free graphs from fractals," Chaos, Solitons & Fractals, Elsevier, vol. 44(8), pages 651-666.
    15. Dong, Gaogao & Tian, Lixin & Du, Ruijin & Fu, Min & Stanley, H. Eugene, 2014. "Analysis of percolation behaviors of clustered networks with partial support–dependence relations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 370-378.
    16. Benatti, Alexandre & da F. Costa, Luciano, 2024. "On the transient and equilibrium features of growing fractal complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    17. Tony H. Grubesic & Timothy C. Matisziw & Alan T. Murray & Diane Snediker, 2008. "Comparative Approaches for Assessing Network Vulnerability," International Regional Science Review, , vol. 31(1), pages 88-112, January.
    18. Knor, Martin & Škrekovski, Riste, 2013. "Deterministic self-similar models of complex networks based on very symmetric graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4629-4637.
    19. Jiao, Bo & Nie, Yuan-ping & Shi, Jian-mai & Huang, Cheng-dong & Zhou, Ying & Du, Jing & Guo, Rong-hua & Tao, Ye-rong, 2016. "Scaling of weighted spectral distribution in deterministic scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 632-645.
    20. Scott Emmons & Stephen Kobourov & Mike Gallant & Katy Börner, 2016. "Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-18, July.

    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.