IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v452y2016icp94-105.html
   My bibliography  Save this article

A novel approach to characterize information radiation in complex networks

Author

Listed:
  • Wang, Xiaoyang
  • Wang, Ying
  • Zhu, Lin
  • Li, Chao

Abstract

The traditional research of information dissemination is mostly based on the virus spreading model that the information is being spread by probability, which does not match very well to the reality, because the information that we receive is always more or less than what was sent. In order to quantitatively describe variations in the amount of information during the spreading process, this article proposes a safety information radiation model on the basis of communication theory, combining with relevant theories of complex networks. This model comprehensively considers the various influence factors when safety information radiates in the network, and introduces some concepts from the communication theory perspective, such as the radiation gain function, receiving gain function, information retaining capacity and information second reception capacity, to describe the safety information radiation process between nodes and dynamically investigate the states of network nodes. On a micro level, this article analyzes the influence of various initial conditions and parameters on safety information radiation through the new model simulation. The simulation reveals that this novel approach can reflect the variation of safety information quantity of each node in the complex network, and the scale-free network has better ”radiation explosive power”, while the small-world network has better ”radiation staying power”. The results also show that it is efficient to improve the overall performance of network security by selecting nodes with high degrees as the information source, refining and simplifying the information, increasing the information second reception capacity and decreasing the noises. In a word, this article lays the foundation for further research on the interactions of information and energy between internal components within complex systems.

Suggested Citation

  • Wang, Xiaoyang & Wang, Ying & Zhu, Lin & Li, Chao, 2016. "A novel approach to characterize information radiation in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 94-105.
  • Handle: RePEc:eee:phsmap:v:452:y:2016:i:c:p:94-105
    DOI: 10.1016/j.physa.2016.01.076
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116001400
    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.2016.01.076?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. L. A. Braunstein & P. A. Macri & J. R. Iglesias, 2012. "Study of a Market Model with Conservative Exchanges on Complex Networks," Papers 1212.1061, arXiv.org, revised Feb 2013.
    2. Yang, Xu-Hua & Wang, Bo & Chen, Sheng-Yong & Wang, Wan-Liang, 2012. "Epidemic dynamics behavior in some bus transport networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 917-924.
    3. Xia, Cheng-yi & Wang, Zhen & Sanz, Joaquin & Meloni, Sandro & Moreno, Yamir, 2013. "Effects of delayed recovery and nonuniform transmission on the spreading of diseases in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(7), pages 1577-1585.
    4. Ni, Shunjiang & Weng, Wenguo & Zhang, Hui, 2011. "Modeling the effects of social impact on epidemic spreading in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4528-4534.
    5. Braunstein, Lidia A. & Macri, Pablo A. & Iglesias, J.R., 2013. "Study of a market model with conservative exchanges on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(8), pages 1788-1794.
    6. Wang, Jiajia & Zhao, Laijun & Huang, Rongbing, 2014. "SIRaRu rumor spreading model in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 43-55.
    7. Wu, Qingchu & Liu, Huaxiang & Small, Michael, 2013. "Dynamical diversity induced by individual responsive immunization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(12), pages 2792-2802.
    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. Fu, Chaoqi & Wang, Ying & Gao, Yangjun & Wang, Xiaoyang, 2017. "Complex networks repair strategies: Dynamic models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 401-406.

    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. Meng, Xiangyi & Zhang, Jian-Wei & Guo, Hong, 2016. "Quantum Brownian motion model for the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 281-288.
    2. Li, Dandan & Ma, Jing, 2017. "How the government’s punishment and individual’s sensitivity affect the rumor spreading in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 284-292.
    3. De Martino, Giuseppe & Spina, Serena, 2015. "Exploiting the time-dynamics of news diffusion on the Internet through a generalized Susceptible–Infected model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 634-644.
    4. Zhang, Yan, 2013. "The impact of other-regarding tendencies on the spatial vaccination game," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 209-215.
    5. Jia, Pingqi & Wang, Chao & Zhang, Gaoyu & Ma, Jianfeng, 2019. "A rumor spreading model based on two propagation channels in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 342-353.
    6. Wei, Daijun & Deng, Xinyang & Zhang, Xiaoge & Deng, Yong & Mahadevan, Sankaran, 2013. "Identifying influential nodes in weighted networks based on evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2564-2575.
    7. Xiang, Zhongyi & Tang, Sanyi & Xiang, Changcheng & Wu, Jianhong, 2015. "On impulsive pest control using integrated intervention strategies," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 930-946.
    8. Sanders, Johnathan & Noble, Benjamin & Van Gorder, Robert A. & Riggs, Cortney, 2012. "Mobility matrix evolution for an SIS epidemic patch model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6256-6267.
    9. Basnarkov, Lasko, 2021. "SEAIR Epidemic spreading model of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    10. Lu, Peng, 2019. "Heterogeneity, judgment, and social trust of agents in rumor spreading," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 447-461.
    11. Zhu, He & Ma, Jing, 2019. "Analysis of SHIR rumor propagation in random heterogeneous networks with dynamic friendships," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 257-271.
    12. Dong, Suyalatu & Deng, Yanbin & Huang, Yong-Chang, 2019. "Exact analytic solution to nonlinear dynamic system of equations for information propagation in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 319-329.
    13. Ran, Maojie & Chen, Jiancu, 2021. "An information dissemination model based on positive and negative interference in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    14. Yao, Yao & Xiao, Xi & Zhang, Chengping & Dou, Changsheng & Xia, Shutao, 2019. "Stability analysis of an SDILR model based on rumor recurrence on social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    15. Jinxian Li & Yanping Hu & Zhen Jin, 2019. "Rumor Spreading of an SIHR Model in Heterogeneous Networks Based on Probability Generating Function," Complexity, Hindawi, vol. 2019, pages 1-15, June.
    16. Yu, Shuzhen & Yu, Zhiyong & Jiang, Haijun, 2024. "A rumor propagation model in multilingual environment with time and state dependent impulsive control," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    17. Yang, Xu-Hua & Lou, Shun-Li & Chen, Guang & Chen, Sheng-Yong & Huang, Wei, 2013. "Scale-free networks via attaching to random neighbors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3531-3536.
    18. Nanath, Krishnadas & Balasubramanian, Sreejith & Shukla, Vinaya & Islam, Nazrul & Kaitheri, Supriya, 2022. "Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    19. Huo, Liang’an & Jiang, Jiehui & Gong, Sixing & He, Bing, 2016. "Dynamical behavior of a rumor transmission model with Holling-type II functional response in emergency event," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 228-240.
    20. Zhu, Linhe & Huang, Xiaoyuan, 2021. "Modeling the dynamics of multi-cluster information propagation in presence of time delay," Chaos, Solitons & Fractals, Elsevier, vol. 146(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:phsmap:v:452:y:2016:i:c:p:94-105. 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.