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A two-stage broadcast message propagation model in social networks

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  • Wang, Dan
  • Cheng, Shun-Jun

Abstract

Message propagation in social networks is becoming a popular topic in complex networks. One of the message types in social networks is called broadcast message. It refers to a type of message which has a unique and unknown destination for the publisher, such as ‘lost and found’. Its propagation always has two stages. Due to this feature, rumor propagation model and epidemic propagation model have difficulty in describing this message’s propagation accurately. In this paper, an improved two-stage susceptible–infected–removed model is proposed. We come up with the concept of the first forwarding probability and the second forwarding probability. Another part of our work is figuring out the influence to the successful message transmission chance in each level resulting from multiple reasons, including the topology of the network, the receiving probability, the first stage forwarding probability, the second stage forwarding probability as well as the length of the shortest path between the publisher and the relevant destination. The proposed model has been simulated on real networks and the results proved the model’s effectiveness.

Suggested Citation

  • Wang, Dan & Cheng, Shun-Jun, 2016. "A two-stage broadcast message propagation model in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1286-1293.
    • Handle: RePEc:eee:phsmap:v:462:y:2016:i:c:p:1286-1293
    DOI: 10.1016/j.physa.2016.07.003
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    References listed on IDEAS

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    1. Xue Zhou & Yong Hu & Yue Wu & Xi Xiong, 2015. "Influence analysis of information erupted on social networks based onSIRmodel," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(02), pages 1-11.
    2. Shi, Hongjing & Duan, Zhisheng & Chen, Guanrong, 2008. "An SIS model with infective medium on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(8), pages 2133-2144.
    3. Freeman, Mark & McVittie, James & Sivak, Iryna & Wu, Jianhong, 2014. "Viral information propagation in the Digg online social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 87-94.
    4. Dottori, M. & Fabricius, G., 2015. "SIR model on a dynamical network and the endemic state of an infectious disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 25-35.
    5. Zhao, Laijun & Wang, Jiajia & Chen, Yucheng & Wang, Qin & Cheng, Jingjing & Cui, Hongxin, 2012. "SIHR rumor spreading model in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2444-2453.
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    Cited by:

    1. Huang, Feihu & Qiao, Shaojie & Peng, Jian & Guo, Bing & Xiong, Xi & Han, Nan, 2019. "A movement model for air passengers based on trip purpose," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 798-808.

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