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Scale-free networks generated by random walkers

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  • Saramäki, Jari
  • Kaski, Kimmo

Abstract

We present a simple mechanism for generating undirected scale-free networks using random walkers, where the network growth is determined by choosing parent vertices by sequential random walks. We show that this mechanism produces scale-free networks with degree exponent γ=3 and clustering coefficients depending on random walk length. The mechanism can be interpreted in terms of preferential attachment without explicit knowledge of node degrees.

Suggested Citation

  • Saramäki, Jari & Kaski, Kimmo, 2004. "Scale-free networks generated by random walkers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 341(C), pages 80-86.
    • Handle: RePEc:eee:phsmap:v:341:y:2004:i:c:p:80-86
    DOI: 10.1016/j.physa.2004.04.110
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    Citations

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    Cited by:

    1. Shriram Ashok Kumar & Maliha Tasnim & Zohvin Singh Basnyat & Faezeh Karimi & Kaveh Khalilpour, 2022. "Resilience Analysis of Australian Electricity and Gas Transmission Networks," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
    2. Ikeda, Nobutoshi, 2021. "Stratified structure of fractal scale-free networks generated by local rules," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    3. Telcs, András & Csernai, Márton & Gulyás, András, 2013. "Load balanced diffusive capture process on homophilic scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(3), pages 510-519.
    4. Ikeda, Nobutoshi, 2019. "Growth model for fractal scale-free networks generated by a random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 424-434.
    5. Xiao, Yunpeng & Wang, Zheng & Li, Qian & Li, Tun, 2019. "Dynamic model of information diffusion based on multidimensional complex network space and social game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 578-590.
    6. S. R. Goldberg & H. Anthony & T. S. Evans, 2015. "Modelling citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1577-1604, December.
    7. Moore, Jack Murdoch & Small, Michael & Yan, Gang, 2021. "Inclusivity enhances robustness and efficiency of social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    8. Ikeda, Nobutoshi, 2015. "Effects of triad formations stimulated by intermediaries on network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 897-908.
    9. Yan Chen & Youran Qi & Qing Liu & Peter Chien, 2018. "Sequential sampling enhanced composite likelihood approach to estimation of social intercorrelations in large-scale networks," Quantitative Marketing and Economics (QME), Springer, vol. 16(4), pages 409-440, December.
    10. Sinan Aral & Paramveer S. Dhillon, 2023. "What (Exactly) Is Novelty in Networks? Unpacking the Vision Advantages of Brokers, Bridges, and Weak Ties," Management Science, INFORMS, vol. 69(2), pages 1092-1115, February.
    11. Colman, E.R. & Rodgers, G.J., 2013. "Complex scale-free networks with tunable power-law exponent and clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5501-5510.
    12. Ghiglino, Christian, 2012. "Random walk to innovation: Why productivity follows a power law," Journal of Economic Theory, Elsevier, vol. 147(2), pages 713-737.
    13. Wu, Zhenyu & Zou, Ming, 2014. "Modeling social tagging using latent interaction potential," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 125-133.

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