IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v466y2017icp243-248.html
   My bibliography  Save this article

Fast approximation of average shortest path length of directed BA networks

Author

Listed:
  • Mao, Guoyong
  • Zhang, Ning

Abstract

The average shortest path length is an important feature for complex networks. However, for large networks, it is very difficult to compute it due to the limitation of computing power. By analyzing the node reachability from several real BA networks as the example, we brought forward the concept of Global Reachable Nodes and Local Reachable Nodes. We found that the average shortest path length of a BA network is determined by the Global Reachable Nodes. From the mechanism of the BA network we illustrated this feature and hereby presented a randomized approximation algorithm for computing the average shortest path length. We verified the accuracy of this algorithm using 8 different networks. For large-scale BA network with millions of nodes, the experiments indicate that our method can estimate its ASPL with high accuracy using only several hundreds of Global Reachable Nodes.

Suggested Citation

  • Mao, Guoyong & Zhang, Ning, 2017. "Fast approximation of average shortest path length of directed BA networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 243-248.
  • Handle: RePEc:eee:phsmap:v:466:y:2017:i:c:p:243-248
    DOI: 10.1016/j.physa.2016.09.025
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116306410
    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.2016.09.025?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. Réka Albert & Hawoong Jeong & Albert-László Barabási, 1999. "Diameter of the World-Wide Web," Nature, Nature, vol. 401(6749), pages 130-131, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Min & Wang, Jinman & Feng, Yu, 2019. "Temporal and spatial change of land use in a large-scale opencast coal mine area: A complex network approach," Land Use Policy, Elsevier, vol. 86(C), pages 375-386.
    2. Xinhai Lu & Yanwei Zhang & Handong Tang, 2021. "Modeling and Simulation of Dissemination of Cultivated Land Protection Policies in China," Land, MDPI, vol. 10(2), pages 1-21, February.

    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. Mohd-Zaid, Fairul & Kabban, Christine M. Schubert & Deckro, Richard F. & White, Edward D., 2017. "Parameter specification for the degree distribution of simulated Barabási–Albert graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 141-152.
    2. Chen, Shu-Heng & Chang, Chia-Ling & Wen, Ming-Chang, 2014. "Social networks and macroeconomic stability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 8, pages 1-40.
    3. Zhang, Wen-Yao & Wei, Zong-Wen & Wang, Bing-Hong & Han, Xiao-Pu, 2016. "Measuring mixing patterns in complex networks by Spearman rank correlation coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 440-450.
    4. Pi, Xiaochen & Tang, Longkun & Chen, Xiangzhong, 2021. "A directed weighted scale-free network model with an adaptive evolution mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    5. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    6. Long Ma & Xiao Han & Zhesi Shen & Wen-Xu Wang & Zengru Di, 2015. "Efficient Reconstruction of Heterogeneous Networks from Time Series via Compressed Sensing," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-12, November.
    7. 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.
    8. Elias Carroni & Paolo Pin & Simone Righi, 2020. "Bring a Friend! Privately or Publicly?," Management Science, INFORMS, vol. 66(5), pages 2269-2290, May.
    9. Biggiero, Lucio & Angelini, Pier Paolo, 2015. "Hunting scale-free properties in R&D collaboration networks: Self-organization, power-law and policy issues in the European aerospace research area," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 21-43.
    10. Duan, Shuyu & Wen, Tao & Jiang, Wen, 2019. "A new information dimension of complex network based on Rényi entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 529-542.
    11. Dávid Csercsik & Sándor Imre, 2017. "Cooperation and coalitional stability in decentralized wireless networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 64(4), pages 571-584, April.
    12. Baek, Seung Ki & Kim, Tae Young & Kim, Beom Jun, 2008. "Testing a priority-based queue model with Linux command histories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3660-3668.
    13. Jing Yang & Yingwu Chen, 2011. "Fast Computing Betweenness Centrality with Virtual Nodes on Large Sparse Networks," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-5, July.
    14. Freddy Hernán Cepeda López, 2008. "La topología de redes como herramienta de seguimiento en el Sistema de Pagos de Alto Valor en Colombia," Borradores de Economia 513, Banco de la Republica de Colombia.
    15. Chung-Yuan Huang & Chuen-Tsai Sun & Hsun-Cheng Lin, 2005. "Influence of Local Information on Social Simulations in Small-World Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-8.
    16. Xiang, Wang, 2023. "Strong ties or structural holes? A distance distribution perspective," Economics Letters, Elsevier, vol. 229(C).
    17. Xue Guo & Hu Zhang & Tianhai Tian, 2019. "Multi-Likelihood Methods for Developing Stock Relationship Networks Using Financial Big Data," Papers 1906.08088, arXiv.org.
    18. Chang, Chia-ling & Chen, Shu-heng, 2011. "Interactions in DSGE models: The Boltzmann-Gibbs machine and social networks approach," Economics Discussion Papers 2011-25, Kiel Institute for the World Economy (IfW Kiel).
    19. Lin, Yi & Zhang, Jianwei & Yang, Bo & Liu, Hong & Zhao, Liping, 2019. "An optimal routing strategy for transport networks with minimal transmission cost and high network capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 551-561.
    20. Stefano Breschi & Lucia Cusmano, 2002. "Unveiling the Texture of a European Research Area: Emergence of Oligarchic Networks under EU Framework Programmes," KITeS Working Papers 130, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Jul 2002.

    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:466:y:2017:i:c:p:243-248. 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.