IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v561y2021ics0378437120306361.html
   My bibliography  Save this article

Coupled propagation dynamics on multiplex activity-driven networks

Author

Listed:
  • Hu, Ping
  • Geng, Dongqing
  • Lin, Tao
  • Ding, Li

Abstract

It is increasingly recognized that human behaviors play an important role in the social information propagation. In this paper, we proposed a novel model coupling the behavior spreading with the information propagation on a two-layer activity-driven network. Based on this model, we explored the influence of behavior spreading on the information propagation process. The outbreak threshold of information propagation was analyzed and the results revealed a two-stage characteristic. Extensive numerical simulations were carried out to illustrate the theoretical results and further investigate the coupled dynamics on multiplex activity-driven networks.

Suggested Citation

  • Hu, Ping & Geng, Dongqing & Lin, Tao & Ding, Li, 2021. "Coupled propagation dynamics on multiplex activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
  • Handle: RePEc:eee:phsmap:v:561:y:2021:i:c:s0378437120306361
    DOI: 10.1016/j.physa.2020.125212
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120306361
    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.2020.125212?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. Karimi, Fariba & Holme, Petter, 2013. "Threshold model of cascades in empirical temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3476-3483.
    2. Li Ding & Ping Hu, 2019. "Contagion Processes on Time-Varying Networks with Homophily-Driven Group Interactions," Complexity, Hindawi, vol. 2019, pages 1-13, October.
    3. Jia, Nan & Ding, Li & Liu, Yu-Jing & Hu, Ping, 2018. "Global stability and optimal control of epidemic spreading on multiplex networks with nonlinear mutual interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 93-105.
    4. Su, Qiang & Huang, Jiajia & Zhao, Xiande, 2015. "An information propagation model considering incomplete reading behavior in microblog," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 55-63.
    5. Liu, Yu & Wang, Bai & Wu, Bin & Shang, Suiming & Zhang, Yunlei & Shi, Chuan, 2016. "Characterizing super-spreading in microblog: An epidemic-based information propagation model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 202-218.
    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. Jia, Mengqi & Li, Xin & Ding, Li, 2021. "Epidemic spreading with awareness on multi-layer activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
    2. Ping Yu & Zhiping Wang & Yanan Sun & Peiwen Wang, 2022. "Risk Diffusion and Control under Uncertain Information Based on Hypernetwork," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    3. Ping Yu & Peiwen Wang & Zhiping Wang & Jia Wang, 2022. "Supply Chain Risk Diffusion Model Considering Multi-Factor Influences under Hypernetwork Vision," Sustainability, MDPI, vol. 14(14), pages 1-15, July.

    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. Tingqiang Chen & Lei Wang & Jining Wang & Qi Yang, 2017. "A Network Diffusion Model of Food Safety Scare Behavior considering Information Transparency," Complexity, Hindawi, vol. 2017, pages 1-16, December.
    2. Hosni, Adil Imad Eddine & Li, Kan & Ahmad, Sadique, 2020. "Analysis of the impact of online social networks addiction on the propagation of rumors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    3. Fariba Karimi & Matthias Raddant, 2016. "Cascades in Real Interbank Markets," Computational Economics, Springer;Society for Computational Economics, vol. 47(1), pages 49-66, January.
    4. Tian, Yang & Tian, Hui & Cui, Yajuan & Zhu, Xuzhen & Cui, Qimei, 2023. "Influence of behavioral adoption preference based on heterogeneous population on multiple weighted networks," Applied Mathematics and Computation, Elsevier, vol. 446(C).
    5. Xianliang Liu & Zishen Yang & Wei Wang, 2021. "The t-latency bounded strong target set selection problem in some kinds of special family of graphs," Journal of Combinatorial Optimization, Springer, vol. 41(1), pages 105-117, January.
    6. Mitja Steinbacher & Matthias Raddant & Fariba Karimi & Eva Camacho Cuena & Simone Alfarano & Giulia Iori & Thomas Lux, 2021. "Advances in the agent-based modeling of economic and social behavior," SN Business & Economics, Springer, vol. 1(7), pages 1-24, July.
    7. Liu, Xiaoyang & He, Daobing & Yang, Linfeng & Liu, Chao, 2019. "A novel negative feedback information dissemination model based on online social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 371-389.
    8. Liu, Qiming & Li, Tao & Sun, Meici, 2017. "The analysis of an SEIR rumor propagation model on heterogeneous network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 372-380.
    9. Jia, Mengqi & Li, Xin & Ding, Li, 2021. "Epidemic spreading with awareness on multi-layer activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
    10. Kobayashi, Teruyoshi & Ogisu, Yoshitaka & Onaga, Tomokatsu, 2023. "Unstable diffusion in social networks," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    11. Xiaole Wan & Zhen Zhang & Chi Zhang & Qingchun Meng, 2020. "Stock Market Temporal Complex Networks Construction, Robustness Analysis, and Systematic Risk Identification: A Case of CSI 300 Index," Complexity, Hindawi, vol. 2020, pages 1-19, July.
    12. Lee, Sang Hoon & Holme, Petter, 2019. "Navigating temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 288-296.
    13. Xiao, Yunpeng & Zhang, Li & Li, Qian & Liu, Ling, 2019. "MM-SIS: Model for multiple information spreading in multiplex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 135-146.
    14. Yue, Zenghui & Xu, Haiyun & Yuan, Guoting & Pang, Hongshen, 2019. "Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: A case study in graphene field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 375-391.
    15. Liu, Xiaoyang & He, Daobing & Liu, Chao, 2018. "Modeling information dissemination and evolution in time-varying online social network based on thermal diffusion motion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 456-476.
    16. Pu, Cun-Lai & Cui, Wei, 2015. "Vulnerability of complex networks under path-based attacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 622-629.
    17. Ma, Ning & Liu, Yijun & Chi, Yuxue, 2018. "Influencer discovery algorithm in a multi-relational network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 415-425.
    18. Zhu, Shu-Shan & Zhu, Xu-Zhen & Wang, Jian-Qun & Zhang, Zeng-Ping & Wang, Wei, 2019. "Social contagions on multiplex networks with heterogeneous population," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 105-113.
    19. Fu, Minglei & Feng, Jun & Lande, Dmytro & Dmytrenko, Oleh & Manko, Dmytro & Prakapovich, Ryhor, 2021. "Dynamic model with super spreaders and lurker users for preferential information propagation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    20. Sang, Chunyan & Li, Tun & Tian, Sirui & Xiao, Yunpeng & Xu, Guangxia, 2019. "SFTRD: A novel information propagation model in heterogeneous networks: Modeling and restraining strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 475-490.

    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:561:y:2021:i:c:s0378437120306361. 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.