SCEIRS information dissemination model based on rumor spreading in complex networks

Document Type : Original Article

Authors

Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran.

Abstract

Complex networks are currently being studied in many fields of science, and many natural systems can be described by them. The Internet and the brain, which are networks of routers and neurons, respectively, are examples of complex networks. There are also different types of complex networks, which can be referred to as scale free networks, small world networks and random networks. In this paper, an epidemic model of rumor spread in all three types of these networks is proposed. In this model, in addition to the existing cases (susceptible-infected-recovered), the rumor delay mechanism as well as the counter-attack mechanism have been added. The proposed model is presented as: Susceptible - Infected - Infected - Counterattack - Recovered - Susceptible (SECIRS). The methods of diffusion and decontamination for these three types of networks are compared. The simulation results are exactly in line with the theoretical analysis and show that in scale free networks, the spread of pollution is faster than the other two types. Pollution is lower and decontamination is faster.

Keywords

References

[1]     S. N. Dorogovtsev and J. F. F. Mendes,“Evolution of networks,” Advances in Physics, vol. 51, pp. 1079-1187, 2002.##
[2]     R. Albert and A. L. Barab, “Statistical mechanics of complex networks,” Rev. Mod. Phys., vol. 74, p. 47, 2002.##
[3]     J. Li, J. Lou, and M. Lou, “Some Discrete SI and SIS Epidemic Models,” Applied Mathematics and Mechanics, vol. 29, pp. 113–119, 2008.##
[4]     F. Zhang, J. Li, and J. Li, “Epidemic characteristics of two classic SIS models with disease-induced death,” Journal of Theoretical Biology, vol. 424, pp. 73-83, 2017.##
[5]     R. Pators-Satorras and A. Vespignani, “Dynamical and Correlation Properties of the Internet,” Phys. Rev. Lett., vol. 86, Nov. 2001.##
[6]     D. J. Daley and D. G. Kendal, “Stochastic Rumours,” J. Inst. Math. Appl., vol. 1, pp. 42–55, March 1965.##
[7]     L. Zhang, M. Small, and K. Judd, “Exactly scale-free scale-free networks,” Physica A: Statistical Mechanics and its Applications, vol. 433, pp. 182-197, 2015.##
[8]     A. Maroosi, E. Zabbah, and H. Ataei Khabbaz, “Network Intrusion Detection using a Combination of  artificial Neural Networks in a Hierarchical Manner,” vol. 1, pp. 89-99, 2020. (In Persian)##
[9]     G. Yiran and M. Fanrong, “Rumor spreading in the online social network: A case of a Renren account,” Third International Conference on Digital Manufacturing & Automation, Guilin, China, 2012.##
[10]  H. Wnag, L. Deng, Y. Huang, and S. Zhao, “A Variant Epidemic Propogation Model Suitable For Rumor Spreading In Online Social Network,” in Proc. International Conference on Machine Learning and Cybernetics, Xian, July 2012.##
[11]  A. Singh, and Y. N. Singh,” Rumor Dynamics and Inoculation of Nodes inWeighted Scale Free Networks with Degree-Degree Correlation,” International Conference on Signal-Image Technology & Internet-Based Systems, Kyoto, Japan, Dec 2013.##
[12]  Q. Wang, Y. Jin, Y. Cui, and S. Cheng, “Rumor Spreading With Nonlinear Infectivities in Weighted Networks,” in Proc. IEEE IC-BNMT, 2013.##
[13]  X. Zhang, Y. Hu, and J. Wang,” Finding Rumor Source in Weighted Scale-Free Networks,” Third International Conference on Cyberspace Technology, Beijing, China, Oct. 2015.##
[14]  Y. Zan, J. Wua, P. Li, and Q. Yu, “SICR rumor spreading model in complex networks Counterattack and  self-resistance,” Physica A: Statistical Mechanics and its Applications, vol. 466, pp. 159-170, 2014.##
[15]  Q. Han, F. Miao, and W. Fan,” Rumor Spreading and Monitoring Deployment in Online Social Networks,” IEEE International Conference on Communication Technology, 2017.##
[16]  G. Cai, M. Bi, and J. Liu, “A Novel Rumor Detection Method Based on Labeled Cascade Propagation Tree,” In: Proceedings of the 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, 2017.##
[17]  M. Jin and F. Liu, “A rumor propagation model based on content trust,” IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, Chengdu, China, March 2019.##
[18]  Y. Zhang, and Z. Chen, “SETQR Propagation Model for Social Networks,” Digital Object Identifier, vol. 7, 2019.##
[19]  H. Wang, T. Li, M. Lei, and J. Liu, “Online and Offine Rumor Spreading Dynamics On Scale-Free Networks,” in proc. Chinese Control Conference, Guangzhou, China, July 2019.##
[20]  L. Zhu and B. Wang, “Stability analysis of a SAIR rumor spreading model with control strategies in online social networks,” Information Sciences, vol. 526,  pp. 1-19, July 2020.##
[21]  L. Ding, P. Hu, Z-H. Guan, and T. Li, “An Efficient Hybrid Control Strategy for Restraining Rumor Spreading,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-13, Jan 2020.##
[22]  L. Di, Y. Gu, G. Qian, and G. X. Yuan, “A Dynamic Epidemic Model for Rumor Spread in Multiplex Network with Numerical Analysis,” Physics Soc., 2020.
[23]  O. P. Kuznetsov, “Complex Networks and Activity Spreading,” Automation and Remote Control, vol. 76, no. 12, pp. 2091–2109, 2015.
[24]  Q. Wu and F. Zhang, “Dynamical behavior of susceptible-infected-recovered-susceptible epidemic model on weighted networks,” Physica A: Statistical Mechanics and its Applications, vol. 491, pp. 82-390, February 2018.
[25]  Y. Wang and J. Cao, “Global Dynamics of a Network Epidemic Model for Waterborne Diseases Spread,” Applied Mathematics and Computation, vol. 237, pp. 474-488, 2014.
Electronic and Cyber Defense
Volume 9, Issue 2 - Serial Number 34
Serial No. 34, Summer Quarterly
June 2021
Pages 121-134

Files

History

  • Receive Date: 15 September 2020
  • Revise Date: 28 October 2020
  • Accept Date: 24 January 2021
  • Publish Date: 22 June 2021

Share

How to cite

Statistics