Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 280-284.doi: 10.11896/jsjkx.210500043

• Big Data & Data Science • Previous Articles     Next Articles

Nonlinear Dynamics Information Dissemination Model Based on Network Media

DU Hong-yi, YANG Hua, LIU Yan-hong, YANG Hong-peng   

  1. College of Information Science and Engineering,Shanxi Agricultural University,Jinzhong,Shanxi 030801,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:DU Hong-yi,born in 1995,postgra-duate.His main research interests include greenhouse environmental control and security of computer network.
    YANG Hua,born in 1974,Ph.D,professor.His main research interests include network security,computer control and control and filtering of nonlinear stochastic systems.
  • Supported by:
    National Natural Science Foundation of China(31671571).

Abstract: Existing network information propagation models that suppose all infected nodes are capable of infecting susceptible nodes,it cannot reflect objectively the fact that information propagation is time-efficient.For this problem,based on mean field theory,from the macro perspective of information dissemination,a novel dynamical model of network media information dissemination is proposed in this paper.According to real situation,the model assumes that only newly infected nodes will infect susceptible nodes in the social network,and there are two ways for susceptible nodes to become infected nodes,one is the spread of infection in social networks,the other is random browsing.Furthermore,based on graph theory,the states of nodes in the model and their transition relations are abstracted as weighted directed graphs.Based on Bayes' theorem,the state transitions between nodes are expressed as probability events,and the probability expression of event occurrence is given.Then the state transformation relationship matrix is determined.Finally,the model is solved using Gauss-Seidel iterative method.Numerical simulation results show that the dissemination of online media information is time-sensitive.Hot events will reach a peak of diffusion in a day,and then the range of diffusion will decline rapidly.Then the statistical data of the hot event from Baidu index is used to verify the validity of the model.The results show that the proposed model can reflect the spreading trend of network information more accurately than existing models.

Key words: Dynamic quarantine, Dynamical model, Network security, Network virus propagation

CLC Number: 

  • TP309
[1] ARNABOLDI V,CONTI M,PASSARELLA A,et al.On line social networks and information diffusion:the role of ego networks[J].Online Social Networks & Media,2017,1(6):44-55.
[2] JIN L,CHEN Y,WANG T,et al.Understanding user behavior in online social networks:A survey[J].IEEE Communications Magazine,2013,51(9):144-150.
[3] LI Y M,SHIU Y L.A diffusion mechanism for social advertising over microblogs[J].Decision Support Systems,2012,54:9-22.
[4] ZHOU D H,HAN W B.DiffRank:A Novel Algorithm for Information Diffusion Detection in Social Networks[J].Chinese Journal of Computers,2014,37(4):884-893.
[5] LIU X Y,HE D B.Research on of Competitive Nonlinear Dynamic Information Diffusion Modeling in Online Social Network[J].Chinese Journal of Computers,2019,42(9):1-20.
[6] SANG C Y,LI T,TIAN S.SFTRD:A novel ingormation propagation model in heterogeneous networks:Modeling and restraining strategy[J].Physica A,2019,524:475-490.
[7] CHIASSERINI C F,GARETTO M,LEONARDI E.Social network de-anonymization under scale-free user relations[J].IEEE/ACM Transactions on Networking,2016,24(6):3756-3769.
[8] KO H,LEE J,PACK S.An opportunistic push scheme for online social networking services in heterogeneous wireless networks[J].IEEE Transactions on Network & Service Management,2017,14(2):416-428.
[9] MA Z E,ZHOU Y C.Mathematical modelling and study on infectious disease dynamics [M].Beijing:Science Press,2004:3-10.
[10] LIU D,YIN Y W,SONG M.Microblog Information Diffusion:Simulation Based on SIR Model[J].Journal of Beijing Univer-sity of Posts and Telecommunications(Social Sciences Edition),2014,16(3):28-33.
[11] WAN Y H,WANG X C.Rumor spreading model with conformity effect[J].Journal of Computer Applications,2016,36(9):2381-2385.
[12] HU X,LI W G,HU Q,et al.SIRS Model and Stability Based on Open Cyber Ecosystem[C]//2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Disco-very.2018.
[13] PIQUEIRA J R C,ARAUJO V O A.Modified epidemiological model for computer viruses[J].Applied Mathematics and Computation,2009,213:355-360.
[14] ZHENG Z R,GUO F,WANG Z F,et al.Study on Microblog Propagation Model Based on Aanlysis of User Behavior[J].Computer Science,2016,43(12):41-46.
[15] CHEN K Y.Empirical analysis of novel coronavirus outbreak based on SEIR model[J].Statistics and Management,2020,35(6):31-38.
[16] YUAN H,CHEN G Q.Network virus-epidemic model with the point-to-group information propagation[J].Applied Mathema-tics and Computation,2008,206:357-367.
[17] SONG L P,ZHANG R P.Dynamical analysis for a malwarepropagation model in wireless sensor network[J].Journal of Measurement of Science and Instrumentation,2016,7(2):136-144.
[18] ZHU G H,JIANG G P,XIA L L.Rumor spreading model considering conformity phenomena in complex social networks[J].Computer Science,2016,43(2):135-139.
[19] CUI Z L,QIAN X D.Research on optimization of blockchain social Network Information Transmission Model[J].Computer Engineering and Applications,2021,57(7):59-69.
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