IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v157y2022ics0960077922001059.html
   My bibliography  Save this article

A novel approach to estimated Boulingand-Minkowski fractal dimension from complex networks

Author

Listed:
  • de Sá, Luiz Alberto Pereira
  • Zielinski, Kallil M.C.
  • Rodrigues, Érick Oliveira
  • Backes, André R.
  • Florindo, João B.
  • Casanova, Dalcimar

Abstract

A complex network presents many topological features which characterize its behavior and dynamics. This characterization is an essential aspect of complex networks analysis and can be performed using several measures, including the fractal dimension. Originally the fractal dimension measures the complexity of an object in a Euclidean space, and the most common methods in the literature to estimate that dimension are box-counting, mass-radius, and Bouligand-Minkowski. However, networks are not Euclidean objects, so that these methods require some adaptation to measure the fractal dimension in this context. The literature presents some adaptations for methods like box-counting and mass-radius. However, there is no known adaptation developed for the Bouligand-Minkowski method. In this way, we propose an adaptation of the Bouligand-Minkowski to measure complex networks’ fractal dimension. We compare our proposed method with others, and we also explore the application of the proposed method in a classification task of complex networks that confirmed its promising potential.

Suggested Citation

  • de Sá, Luiz Alberto Pereira & Zielinski, Kallil M.C. & Rodrigues, Érick Oliveira & Backes, André R. & Florindo, João B. & Casanova, Dalcimar, 2022. "A novel approach to estimated Boulingand-Minkowski fractal dimension from complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    • Handle: RePEc:eee:chsofr:v:157:y:2022:i:c:s0960077922001059
    DOI: 10.1016/j.chaos.2022.111894
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922001059
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2022.111894?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. M. E. J. Newman & D. J. Watts, 1999. "Scaling and Percolation in the Small-World Network Model," Working Papers 99-05-034, Santa Fe Institute.
    2. 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.
    3. Wen, Tao & Jiang, Wen, 2018. "An information dimension of weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 388-399.
    4. Silva, Pedro M. & Florindo, Joao B., 2019. "A statistical descriptor for texture images based on the box counting fractal dimension," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 528(C).
    5. Chaoming Song & Shlomo Havlin & Hernán A. Makse, 2005. "Self-similarity of complex networks," Nature, Nature, vol. 433(7024), pages 392-395, January.
    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. Zhao, Tong & Li, Zhen & Deng, Yong, 2023. "Information fractal dimension of Random Permutation Set," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

    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. 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.
    2. 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.
    3. Lei, Mingli, 2022. "Information dimension based on Deng entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    4. Pavón-Domínguez, Pablo & Moreno-Pulido, Soledad, 2022. "Sandbox fixed-mass algorithm for multifractal unweighted complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    5. Ramirez-Arellano, Aldo & Hernández-Simón, Luis Manuel & Bory-Reyes, Juan, 2020. "A box-covering Tsallis information dimension and non-extensive property of complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    6. Sun, Lina & Huang, Ning & Li, Ruiying & Bai, Yanan, 2019. "A new fractal reliability model for networks with node fractal growth and no-loop," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 699-707.
    7. Pavón-Domínguez, Pablo & Moreno-Pulido, Soledad, 2020. "A Fixed-Mass multifractal approach for unweighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    8. Lahtinen, Jani & Kertész, János & Kaski, Kimmo, 2005. "Sandpiles on Watts–Strogatz type small-worlds," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 349(3), pages 535-547.
    9. Yao, Jialing & Sun, Bingbin & Xi, lifeng, 2019. "Fractality of evolving self-similar networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 211-216.
    10. 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).
    11. Liu, Run-Ran & Chu, Changchang & Meng, Fanyuan, 2023. "Higher-order interdependent percolation on hypergraphs," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    12. Muneeb A Khan & Muazzam A Khan & Anis U Rahman & Asad Waqar Malik & Safdar A Khan, 2019. "Exploiting cooperative sensing for accurate target tracking in industrial Internet of things," International Journal of Distributed Sensor Networks, , vol. 15(12), pages 15501477198, December.
    13. Sun, Bingbin & Yao, Jialing & Xi, Lifeng, 2019. "Eigentime identities of fractal sailboat networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 338-349.
    14. Lu, Qing-Chang & Xu, Peng-Cheng & Zhao, Xiangmo & Zhang, Lei & Li, Xiaoling & Cui, Xin, 2022. "Measuring network interdependency between dependent networks: A supply-demand-based approach," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    15. Lambiotte, R. & Panzarasa, P., 2009. "Communities, knowledge creation, and information diffusion," Journal of Informetrics, Elsevier, vol. 3(3), pages 180-190.
    16. Floortje Alkemade & Carolina Castaldi, 2005. "Strategies for the Diffusion of Innovations on Social Networks," Computational Economics, Springer;Society for Computational Economics, vol. 25(1), pages 3-23, February.
    17. Zhang, Qi & Luo, Chuanhai & Li, Meizhu & Deng, Yong & Mahadevan, Sankaran, 2015. "Tsallis information dimension of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 707-717.
    18. Aldrich, Preston R. & El-Zabet, Jermeen & Hassan, Seerat & Briguglio, Joseph & Aliaj, Enela & Radcliffe, Maria & Mirza, Taha & Comar, Timothy & Nadolski, Jeremy & Huebner, Cynthia D., 2015. "Monte Carlo tests of small-world architecture for coarse-grained networks of the United States railroad and highway transportation systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 32-39.
    19. Huo, Liang’an & Song, Naixiang, 2016. "Dynamical interplay between the dissemination of scientific knowledge and rumor spreading in emergency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 73-84.
    20. Retière, N. & Sidqi, Y. & Frankhauser, P., 2022. "A steady-state analysis of distribution networks by diffusion-limited-aggregation and multifractal geometry," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).

    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:chsofr:v:157:y:2022:i:c:s0960077922001059. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.