IDEAS home Printed from https://ideas.repec.org/p/wop/safiwp/99-09-066.html
   My bibliography  Save this paper

Mean-Field Solution of the Small-World Network Model

Author

Listed:
  • M. E. J. Newman
  • C. Moore
  • D. J. Watts

Abstract

The small-world network model is a simple model of the structure of social networks, which simultaneously possesses characteristics of both regular lattices and random graphs. The model consists of a one-dimensional lattice with a low density of shortcuts added between randomly selected pairs of points. These shortcuts greatly reduce the typical path length between any two points on the lattice. We present a mean-field solution for the average path length and for the distribution of path lengths in the model. This solution is exact in the limit of large system size and either large or small number of shortcuts.

Suggested Citation

  • M. E. J. Newman & C. Moore & D. J. Watts, 1999. "Mean-Field Solution of the Small-World Network Model," Working Papers 99-09-066, Santa Fe Institute.
  • Handle: RePEc:wop:safiwp:99-09-066
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    References listed on IDEAS

    as
    1. A. Barrat & M. Weigt, 2000. "On the properties of small-world network models," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 13(3), pages 547-560, February.
    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. Konstantinos Antoniadis & Kostas Zafiropoulos & Vasiliki Vrana, 2016. "A Method for Assessing the Performance of e-Government Twitter Accounts," Future Internet, MDPI, vol. 8(2), pages 1-18, April.
    2. Yue Chen & Xiaojian Niu & Yan Zhang, 2019. "Exploring Contrarian Degree in the Trading Behavior of China's Stock Market," Complexity, Hindawi, vol. 2019, pages 1-12, April.
    3. Daniel Felix Ahelegbey & Luis Carvalho & Eric D. Kolaczyk, 2020. "A Bayesian Covariance Graph And Latent Position Model For Multivariate Financial Time Series," DEM Working Papers Series 181, University of Pavia, Department of Economics and Management.
    4. Lawford, Steve & Mehmeti, Yll, 2020. "Cliques and a new measure of clustering: With application to U.S. domestic airlines," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    5. Tsonis, Anastasios A. & Swanson, Kyle L. & Wang, Geli, 2008. "Estimating the clustering coefficient in scale-free networks on lattices with local spatial correlation structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5287-5294.
    6. Pollner, Péter & Palla, Gergely & Vicsek, Tamás, 2010. "Clustering of tag-induced subgraphs in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5887-5894.
    7. Ding Ding & Liyan Han & Libo Yin, 2017. "Systemic risk and dynamics of contagion: a duplex inter-bank network," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1435-1445, September.
    8. Guida, Michele & Maria, Funaro, 2007. "Topology of the Italian airport network: A scale-free small-world network with a fractal structure?," Chaos, Solitons & Fractals, Elsevier, vol. 31(3), pages 527-536.
    9. Elena Agliari & Adriano Barra & Andrea Galluzzi & Marco Alberto Javarone & Andrea Pizzoferrato & Daniele Tantari, 2015. "Emerging Heterogeneities in Italian Customs and Comparison with Nearby Countries," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-24, December.
    10. Hossein Sabzian & Mohammad Ali Shafia & Mehdi Ghazanfari & Ali Bonyadi Naeini, 2020. "Modeling the Adoption and Diffusion of Mobile Telecommunications Technologies in Iran: A Computational Approach Based on Agent-Based Modeling and Social Network Theory," Sustainability, MDPI, vol. 12(7), pages 1-36, April.
    11. Gian Paolo Clemente & Marco Fattore & Rosanna Grassi, 2018. "Structural comparisons of networks and model-based detection of small-worldness," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(1), pages 117-141, April.
    12. Robert Boyer & Denis Boyer & Gilles Laferté, 2007. "La connexion des réseaux comme facteur de changement institutionnel : l'exemple des vins de Bourgogne," PSE Working Papers halshs-00587708, HAL.
    13. Marr, Carsten & Hütt, Marc-Thorsten, 2005. "Topology regulates pattern formation capacity of binary cellular automata on graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 641-662.
    14. Bombelli, Alessandro & Santos, Bruno F. & Tavasszy, Lóránt, 2020. "Analysis of the air cargo transport network using a complex network theory perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    15. Panchenko, Valentyn & Gerasymchuk, Sergiy & Pavlov, Oleg V., 2013. "Asset price dynamics with heterogeneous beliefs and local network interactions," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2623-2642.
    16. Jie Yu & Fei You & Jian Wang & Zishan Wang, 2023. "Evolution Modes of Chili Pepper Industry Clusters under the Perspective of Social Network—An Example from Xinfu District, Xinzhou, Shanxi Province," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    17. Li, Chunguang, 2009. "Memorizing morph patterns in small-world neuronal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(2), pages 240-246.
    18. Cristopher Moore & M. E. J. Newman, 2000. "Exact Solution of Site and Bond Percolation on Small-World Networks," Working Papers 00-01-007, Santa Fe Institute.
    19. Jason Cory Brunson & Steve Fassino & Antonio McInnes & Monisha Narayan & Brianna Richardson & Christopher Franck & Patrick Ion & Reinhard Laubenbacher, 2014. "Evolutionary events in a mathematical sciences research collaboration network," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(3), pages 973-998, June.
    20. Dassisti, M. & Carnimeo, L., 2013. "A small-world methodology of analysis of interchange energy-networks: The European behaviour in the economical crisis," Energy Policy, Elsevier, vol. 63(C), pages 887-899.

    More about this item

    Keywords

    Small worlds; social networks; mean-field theory.;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:wop:safiwp:99-09-066. 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: Thomas Krichel (email available below). General contact details of provider: https://edirc.repec.org/data/epstfus.html .

    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.