IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v507y2018icp12-21.html
   My bibliography  Save this article

Prediction of competitive diffusion on complex networks

Author

Listed:
  • Zhao, Jiuhua
  • Liu, Qipeng
  • Wang, Lin
  • Wang, Xiaofan

Abstract

In this paper, we study the prediction problem of diffusion process on complex networks in competitive circumstances. With this problem solved, the competitors could timely intervene the diffusion process if needed such that an expected outcome might be obtained. We consider a model with two groups of competitors spreading opposite opinions on a network. A prediction method based on the mutual influences among the agents is proposed, called Influence Matrix (IM for short), and simulations on real-world networks show that the proposed IM method has quite high accuracy on predicting both the preference of any normal agent and the final competition result. For comparison purpose, classic centrality measures are also used to predict the competition result. It is shown that PageRank, Degree, Katz Centrality, and the IM method are suitable for predicting the competition result. More precisely, in undirected networks, the IM method performs better than these centrality measures when the competing group contains more than one agent; in directed networks, the IM method performs only second to PageRank.

Suggested Citation

  • Zhao, Jiuhua & Liu, Qipeng & Wang, Lin & Wang, Xiaofan, 2018. "Prediction of competitive diffusion on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 12-21.
    • Handle: RePEc:eee:phsmap:v:507:y:2018:i:c:p:12-21
    DOI: 10.1016/j.physa.2018.05.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118305405
    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.2018.05.004?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. Fortunato, Santo, 2005. "Damage spreading and opinion dynamics on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 683-690.
    2. Jalili, Mahdi, 2013. "Social power and opinion formation in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 959-966.
    3. D’Agostino, Gregorio & D’Antonio, Fulvio & De Nicola, Antonio & Tucci, Salvatore, 2015. "Interests diffusion in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 443-461.
    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. Huang, Yubo & Dong, Hongli & Zhang, Weidong & Lu, Junguo, 2019. "Stability analysis of nonlinear oscillator networks based on the mechanism of cascading failures," Chaos, Solitons & Fractals, Elsevier, vol. 128(C), pages 5-15.
    2. Wang, Haiying & Moore, Jack Murdoch & Small, Michael & Wang, Jun & Yang, Huijie & Gu, Changgui, 2022. "Epidemic dynamics on higher-dimensional small world networks," Applied Mathematics and Computation, Elsevier, vol. 421(C).
    3. Geng, Yang & Zhang, Yulin, 2020. "Platform launch in two-sided markets and users’ expectations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(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. Yoon, Jisung & Park, Jinseo & Yun, Jinhyuk & Jung, Woo-Sung, 2023. "Quantifying knowledge synchronization with the network-driven approach," Journal of Informetrics, Elsevier, vol. 17(4).
    2. Xu, Yuxin & Gao, Fei, 2024. "A novel higher-order Deffuant–Weisbuch networks model incorporating the Susceptible Infected Recovered framework," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    3. Li, Mingwu & Dankowicz, Harry, 2019. "Impact of temporal network structures on the speed of consensus formation in opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1355-1370.
    4. Shane T. Mueller & Yin-Yin Sarah Tan, 2018. "Cognitive perspectives on opinion dynamics: the role of knowledge in consensus formation, opinion divergence, and group polarization," Journal of Computational Social Science, Springer, vol. 1(1), pages 15-48, January.
    5. Evangelos Ioannidis & Nikos Varsakelis & Ioannis Antoniou, 2020. "Promoters versus Adversaries of Change: Agent-Based Modeling of Organizational Conflict in Co-Evolving Networks," Mathematics, MDPI, vol. 8(12), pages 1-25, December.
    6. Castro, Luis E. & Shaikh, Nazrul I., 2018. "A particle-learning-based approach to estimate the influence matrix of online social networks," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 1-18.
    7. Al Khafaf, Nameer & Jalili, Mahdi, 2019. "Optimization of synchronizability in complex spatial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 46-55.
    8. Antonio De Nicola & Gregorio D’Agostino, 2021. "Assessment of gender divide in scientific communities," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3807-3840, May.
    9. Yingcheng Xu & Li Wang & Bo Xu & Wei Jiang & Chaoqun Deng & Fang Ji & Xiaobo Xu, 0. "An information integration and transmission model of multi-source data for product quality and safety," Information Systems Frontiers, Springer, vol. 0, pages 1-22.
    10. Cai, Xing & Xia, Wei & Huang, Weihua & Yang, Haijun, 2024. "Dynamics of momentum in financial markets based on the information diffusion in complex social networks," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
    11. Huang, Changwei & Bian, Huanyu & Han, Wenchen, 2024. "Breaking the symmetry neutralizes the extremization under the repulsion and higher order interactions," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    12. Han, Wenchen & Feng, Yuee & Qian, Xiaolan & Yang, Qihui & Huang, Changwei, 2020. "Clusters and the entropy in opinion dynamics on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    13. Cheng, Xiu & Long, Ruyin & Chen, Hong & Yang, Jiahui, 2019. "Does social interaction have an impact on residents’ sustainable lifestyle decisions? A multi-agent stimulation based on regret and game theory," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    14. Teo Victor Silva & Sebastián Gonçalves & Bruno Requião Cunha, 2024. "Bounded confidence opinion dynamics with Asch-like social conformity in complex networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(9), pages 1-10, September.
    15. Gong, Hao & Guo, Chunxiang & Liu, Yu, 2021. "Measuring network rationality and simulating information diffusion based on network structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    16. Bian, Tian & Hu, Jiantao & Deng, Yong, 2017. "Identifying influential nodes in complex networks based on AHP," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 422-436.
    17. Zhong, Li-Xin & Xu, Wen-Juan & Chen, Rong-Da & Zhong, Chen-Yang & Qiu, Tian & Shi, Yong-Dong & Wang, Li-Liang, 2016. "A generalized voter model with time-decaying memory on a multilayer network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 95-105.
    18. Yingcheng Xu & Li Wang & Bo Xu & Wei Jiang & Chaoqun Deng & Fang Ji & Xiaobo Xu, 2019. "An information integration and transmission model of multi-source data for product quality and safety," Information Systems Frontiers, Springer, vol. 21(1), pages 191-212, February.
    19. Sznajd-Weron, Katarzyna & Sznajd, Józef & Weron, Tomasz, 2021. "A review on the Sznajd model — 20 years after," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    20. Tian, Ru-Ya & Wu, Lei & Liang, Xiao-He & Zhang, Xue-Fu, 2018. "Opinion data mining based on DNA method and ORA software," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1471-1480.

    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:507:y:2018:i:c:p:12-21. 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.