IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v188y2024ics0960077924010385.html
   My bibliography  Save this article

Type-II Apollonian network: More robust and more efficient Apollonian network

Author

Listed:
  • Ma, Fei
  • Ouyang, Jinzhi
  • Shi, Haobin
  • Pan, Wei
  • Wang, Ping

Abstract

The family of planar graphs is a particularly important family and models many networks including the layout of printed circuits. The widely-known Apollonian packing process has been used as guideline to create the typical Apollonian network with planarity. In this paper, we propose a new principled framework based on the Apollonian packing process to generate model as complex network, and obtain a family of new networks called Type-II Apollonian network At. While our network and the typical Apollonian network are maximal planar, the former turns out to be Hamiltonian and Eulerian, however, the latter is not. Then, we in-depth study some fundamental structural properties on network At, and verify that network At is sparse, has scale-free feature and small-world property, and exhibits disassortative mixing structure. Next, we derive the asymptotic solution of the spanning tree entropy of network At by designing an effective algorithm, which suggests that Type-II Apollonian network is more robust to a random removal of edges than the typical Apollonian network. Additionally, we study trapping problem on network At, and use average trapping time as metric to show that Type-II Apollonian network At has more efficient underlying structure for fast information diffusion than the typical Apollonian network.

Suggested Citation

  • Ma, Fei & Ouyang, Jinzhi & Shi, Haobin & Pan, Wei & Wang, Ping, 2024. "Type-II Apollonian network: More robust and more efficient Apollonian network," Chaos, Solitons & Fractals, Elsevier, vol. 188(C).
  • Handle: RePEc:eee:chsofr:v:188:y:2024:i:c:s0960077924010385
    DOI: 10.1016/j.chaos.2024.115486
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077924010385
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2024.115486?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. Szabó, Gábor J. & Alava, Mikko & Kertész, János, 2003. "Geometry of minimum spanning trees on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 330(1), pages 31-36.
    2. Lima, F.W.S. & Sumour, Muneer A. & Moreira, André A. & Araújo, Ascânio D., 2021. "Non-equilibrium BCS model on Apollonian networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    3. Ri, SongIl, 2020. "Fractal functions on the Sierpinski Gasket," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    4. Chaoming Song & Shlomo Havlin & Hernán A. Makse, 2005. "Self-similarity of complex networks," Nature, Nature, vol. 433(7024), pages 392-395, January.
    5. Alves, G.A. & Alves, T.F.A. & Lima, F.W.S. & Macedo-Filho, A., 2021. "Consensus formation on Apollonian networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    6. Jia-Bao Liu & Zahid Raza & Muhammad Javaid, 2020. "Zagreb Connection Numbers for Cellular Neural Networks," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-8, October.
    7. Gujun Wang & Feng Zhu, 2023. "Counting spanning trees of generalized n-edges Apollonian networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 34(09), pages 1-11, September.
    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. Djauhari, Maman Abdurachman & Gan, Siew Lee, 2015. "Optimality problem of network topology in stocks market analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 108-114.
    2. 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.
    3. 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.
    4. 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).
    5. 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.
    6. 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).
    7. Lambiotte, R. & Panzarasa, P., 2009. "Communities, knowledge creation, and information diffusion," Journal of Informetrics, Elsevier, vol. 3(3), pages 180-190.
    8. 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.
    9. 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.
    10. 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).
    11. 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.
    12. Meng, Xiangyi & Zhou, Bin, 2023. "Scale-free networks beyond power-law degree distribution," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    13. Yin, Likang & Deng, Yong, 2018. "Measuring transferring similarity via local information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 498(C), pages 102-115.
    14. Fan Xu & Zeng Zhou & Sergio Fagherazzi & Andrea D’Alpaos & Ian Townend & Kun Zhao & Weiming Xie & Leicheng Guo & Xianye Wang & Zhong Peng & Zhicheng Yang & Chunpeng Chen & Guangcheng Cheng & Yuan Xu &, 2024. "Anomalous scaling of branching tidal networks in global coastal wetlands and mudflats," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    15. Rosenberg, Eric, 2018. "Generalized Hausdorff dimensions of a complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 1-17.
    16. Xuezai Pan & Xudong Shang, 2022. "The Uniform Convergence Property of Sequence of Fractal Interpolation Functions in Complicated Networks," Mathematics, MDPI, vol. 10(20), pages 1-8, October.
    17. Kowsalya, P. & Mohanrasu, S.S. & Kashkynbayev, Ardak & Gokul, P. & Rakkiyappan, R., 2024. "Fixed-time synchronization of Inertial Cohen-Grossberg Neural Networks with state dependent delayed impulse control and its application to multi-image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    18. Ou, Ruiqiu & Yang, Jianmei, 2012. "On structural properties of scale-free networks with finite size," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 887-894.
    19. Li, Jun-fang & Zhang, Bu-han & Liu, Yi-fang & Wang, Kui & Wu, Xiao-shan, 2012. "Spatial evolution character of multi-objective evolutionary algorithm based on self-organized criticality theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5490-5499.
    20. Li, Meizhu & Zhang, Qi & Deng, Yong, 2018. "Evidential identification of influential nodes in network of networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 283-296.

    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:188:y:2024:i:c:s0960077924010385. 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.