IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v511y2018icp1-17.html
   My bibliography  Save this article

Generalized Hausdorff dimensions of a complex network

Author

Listed:
  • Rosenberg, Eric

Abstract

The box counting dimension dB of a complex network G, and the generalized dimensions {Dq,q∈R} of G, have been well studied. However, the Hausdorff dimension dH of a geometric object, which generalizes dB by not assuming equal-diameter boxes, has not previously been extended to G. Similarly, the generalized Hausdorff dimensions {DqH,q∈R} of a geometric object (defined by Grassberger in 1985), which extend the generalized dimensions Dq by not assuming equal-diameter boxes, have not previously been extended to G. In this paper we first develop a definition of dH for G and compare dH to dB on both constructed and real-world networks. Then we extend Grassberger’s work by defining the generalized Hausdorff dimensions DqH of G, and computing the DqH vs. q multifractal spectrum for several networks. Given a minimal covering B(s) of G for a range S of box sizes, computing dH utilizes the diameter of each box in B(s) for s∈S, and computing DqH utilizes the diameter and mass of each box in B(s). Also, computing dB and Dq (for a given q) typically utilizes linear regression; in contrast, computing dH and DqH (for a given q) requires minimizing a function of one variable. Computational results show that dH can sometimes be more useful than dB in quantifying changes in the topology of a network. However, dH is harder to compute than dB, and DqH is less well behaved than Dq. We conclude that dH and DqH should be added to the set of useful metrics for characterizing a complex network, but they cannot be expected to replace dB and Dq.

Suggested Citation

  • Rosenberg, Eric, 2018. "Generalized Hausdorff dimensions of a complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 1-17.
  • Handle: RePEc:eee:phsmap:v:511:y:2018:i:c:p:1-17
    DOI: 10.1016/j.physa.2018.06.121
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118308446
    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.2018.06.121?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. Gallos, Lazaros K. & Song, Chaoming & Makse, Hernán A., 2007. "A review of fractality and self-similarity in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(2), pages 686-691.
    2. Pablo M. Gleiser & Leon Danon, 2003. "Community Structure In Jazz," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 565-573.
    3. Chaoming Song & Shlomo Havlin & Hernán A. Makse, 2005. "Self-similarity of complex networks," Nature, Nature, vol. 433(7024), pages 392-395, January.
    4. Xingyuan Wang & Zhenzhen Liu & Mogei Wang, 2013. "The Correlation Fractal Dimension Of Complex Networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 24(05), pages 1-9.
    5. Li, Dongyan & Wang, Xingyuan & Huang, Penghe, 2017. "A fractal growth model: Exploring the connection pattern of hubs in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 200-211.
    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. 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.
    2. Ikeda, Nobutoshi, 2020. "Fractal networks induced by movements of random walkers on a tree graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    3. Xi, Lifeng & Wang, Lihong & Wang, Songjing & Yu, Zhouyu & Wang, Qin, 2017. "Fractality and scale-free effect of a class of self-similar networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 31-40.
    4. Feng, Shiyuan & Weng, Tongfeng & Chen, Xiaolu & Ren, Zhuoming & Su, Chang & Li, Chunzi, 2024. "Scaling law of diffusion processes on fractal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    5. Rasul Kochkarov & Azret Kochkarov, 2022. "Introduction to the Class of Prefractal Graphs," Mathematics, MDPI, vol. 10(14), pages 1-17, July.
    6. Xie, Wen-Jie & Zhou, Wei-Xing, 2011. "Horizontal visibility graphs transformed from fractional Brownian motions: Topological properties versus the Hurst index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3592-3601.
    7. Yu-Hsiang Fu & Chung-Yuan Huang & Chuen-Tsai Sun, 2017. "A community detection algorithm using network topologies and rule-based hierarchical arc-merging strategies," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-30, November.
    8. 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.
    9. 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).
    10. Chen, Jin & Le, Anbo & Wang, Qin & Xi, Lifeng, 2016. "A small-world and scale-free network generated by Sierpinski Pentagon," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 126-135.
    11. Fu, Yu-Hsiang & Huang, Chung-Yuan & Sun, Chuen-Tsai, 2016. "Using a two-phase evolutionary framework to select multiple network spreaders based on community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 840-853.
    12. Maiorino, Enrico & Livi, Lorenzo & Giuliani, Alessandro & Sadeghian, Alireza & Rizzi, Antonello, 2015. "Multifractal characterization of protein contact networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 302-313.
    13. Ku, Seungmo & Lee, Changju & Chang, Woojin & Wook Song, Jae, 2020. "Fractal structure in the S&P500: A correlation-based threshold network approach," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    14. Lei, Mingli, 2022. "Information dimension based on Deng entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    15. Wei, Bo & Deng, Yong, 2019. "A cluster-growing dimension of complex networks: From the view of node closeness centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 80-87.
    16. Jiang, Jincheng & Xu, Zhihua & Zhang, Zhenxin & Zhang, Jie & Liu, Kang & Kong, Hui, 2023. "Revealing the fractal and self-similarity of realistic collective human mobility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    17. Craig, Adam & Yücel, Mesut & Muchnik, Lev & Hershberg, Uri, 2022. "Impact of finite size effect on applicability of generalized fractal and spectral dimensions to biological networks," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    18. Zhou, Wei-Xing & Jiang, Zhi-Qiang & Sornette, Didier, 2007. "Exploring self-similarity of complex cellular networks: The edge-covering method with simulated annealing and log-periodic sampling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 741-752.
    19. 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.
    20. Werner, Gerhard, 2013. "Consciousness viewed in the framework of brain phase space dynamics, criticality, and the Renormalization Group," Chaos, Solitons & Fractals, Elsevier, vol. 55(C), pages 3-12.

    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:511:y:2018:i:c:p:1-17. 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.