Message Board

Dear readers, authors and reviewers,you can add a message on this page. We will reply to you as soon as possible!

2023 Volume 48 Issue 8
Article Contents

ZHAO Yongsheng, HOU Qiang, ZHANG Lei. Dynamic Analysis of SIMR Rumor Spreading Model with Cross-Transmission[J]. Journal of Southwest China Normal University(Natural Science Edition), 2023, 48(8): 26-32. doi: 10.13718/j.cnki.xsxb.2023.08.004
Citation: ZHAO Yongsheng, HOU Qiang, ZHANG Lei. Dynamic Analysis of SIMR Rumor Spreading Model with Cross-Transmission[J]. Journal of Southwest China Normal University(Natural Science Edition), 2023, 48(8): 26-32. doi: 10.13718/j.cnki.xsxb.2023.08.004

Dynamic Analysis of SIMR Rumor Spreading Model with Cross-Transmission

More Information
  • Corresponding author: HOU Qiang ; 
  • Received Date: 29/10/2022
    Available Online: 20/08/2023
  • MSC: O175.1

  • In this paper, a SIMR rumor spreading model is established with individual and medium cross-transmission based on the rumor spreading mechanism, and the impact of cross-transmission on the rumor spreading is studied. Firstly, the basic reproduction number R0 is obtained by using the next-generation matrix method, the global asymptotic stability of the rumor-free equilibrium point is proved when R0≤1. Further, the uniformly persistence of the model and the unique of rumor-prevailing equilibrium point are proved when R0 > 1. Finally, the global asymptotic stability of the rumor-prevailing equilibrium point is proved by using Lyapunov functions and graph theories. These results indicate that cross-transmission affects the basic reproduction number but not affects the global dynamic attribute of rumor spreading.

  • 加载中
  • [1] 谈谷铮. 对谣言的社会学剖析[J]. 社会科学, 1989(10): 30-34.

    Google Scholar

    [2] ZHAO L J, XIE W L, GAO H O, et al. A Rumor Spreading Model with Variable Forgetting Rate[J]. Physica A: Statistical Mechanics and Its Applications, 2013, 392(23): 6146-6154. doi: 10.1016/j.physa.2013.07.080

    CrossRef Google Scholar

    [3] XIA L L, JIANG G P, SONG B, et al. Rumor Spreading Model Considering Hesitating Mechanism in Complex Social Networks[J]. Physica A: Statistical Mechanics and Its Applications, 2015, 437: 295-303. doi: 10.1016/j.physa.2015.05.113

    CrossRef Google Scholar

    [4] HU Y H, PAN Q H, HOU W B, et al. Rumor Spreading Model with the Different Attitudes towards Rumors[J]. Physica A: Statistical Mechanics and Its Applications, 2018, 502: 331-344. doi: 10.1016/j.physa.2018.02.096

    CrossRef Google Scholar

    [5] XIA Y, JIANG H J, YU Z. Global Dynamics of ILSR Rumor Spreading Model with General Nonlinear Spreading Rate in Multi-Lingual Environment[J]. Chaos, Solitons & Fractals, 2022, 154: 111698.

    Google Scholar

    [6] LI J R, JIANG H J, HU C, et al. Dynamical Analysis of Rumor Spreading Model in Homogeneous Complex Networks[J]. Applied Mathematics and Computation, 2019, 359: 374-385. doi: 10.1016/j.amc.2019.04.076

    CrossRef Google Scholar

    [7] WANG J L, JIANG H J, MA T L, et al. Global Dynamics of the Multi-Lingual SIR Rumor Spreading Model with Cross-Transmitted Mechanism[J]. Chaos, Solitons & Fractals, 2019, 126: 148-157.

    Google Scholar

    [8] VAN DEN DRIESSCHE P, WATMOUGH J. Reproduction Numbers and Sub-Threshold Endemic Equilibria for Compartmental Models of Disease Transmission[J]. Mathematical Biosciences, 2002, 180(1-2): 29-48. doi: 10.1016/S0025-5564(02)00108-6

    CrossRef Google Scholar

    [9] LASALLE J P. New Stability Results for Nonautonomous Systems[M]//Dynamical Systems. Amsterdam: Elsevier, 1977: 175-183.

    Google Scholar

    [10] 刘萍. 具时滞与反馈控制的企业集群和第三方物流的依托型共生模型的持久性[J]. 云南大学学报(自然科学版), 2016, 38(1): 1-10.

    Google Scholar

    [11] THIEME H R. Persistence under Relaxed Point-Dissipativity (with Application to an Endemic Model)[J]. SIAM Journal on Mathematical Analysis, 1993, 24(2): 407-435. doi: 10.1137/0524026

    CrossRef Google Scholar

    [12] WANG W D, ZHAO X Q. An Epidemic Model in a Patchy Environment[J]. Mathematical Biosciences, 2004, 190(1): 97-112. doi: 10.1016/j.mbs.2002.11.001

    CrossRef Google Scholar

    [13] LAJMANOVICH A, YORKE J A. A Deterministic Model for Gonorrhea in a Nonhomogeneous Population[J]. Mathematical Biosciences, 1976, 28(3-4): 221-236. doi: 10.1016/0025-5564(76)90125-5

    CrossRef Google Scholar

    [14] LI M Y, SHUAI Z S. Global-Stability Problem for Coupled Systems of Differential Equations on Networks[J]. Journal of Differential Equations, 2010, 248(1): 1-20. doi: 10.1016/j.jde.2009.09.003

    CrossRef Google Scholar

  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article Metrics

Article views(2237) PDF downloads(346) Cited by(0)

Access History

Other Articles By Authors

Dynamic Analysis of SIMR Rumor Spreading Model with Cross-Transmission

    Corresponding author: HOU Qiang ; 

Abstract: 

In this paper, a SIMR rumor spreading model is established with individual and medium cross-transmission based on the rumor spreading mechanism, and the impact of cross-transmission on the rumor spreading is studied. Firstly, the basic reproduction number R0 is obtained by using the next-generation matrix method, the global asymptotic stability of the rumor-free equilibrium point is proved when R0≤1. Further, the uniformly persistence of the model and the unique of rumor-prevailing equilibrium point are proved when R0 > 1. Finally, the global asymptotic stability of the rumor-prevailing equilibrium point is proved by using Lyapunov functions and graph theories. These results indicate that cross-transmission affects the basic reproduction number but not affects the global dynamic attribute of rumor spreading.

  • 谣言是指没有事实根据的传闻,它常常伴随一些重要的事件产生,谣言歪曲事件的实际情况,可能会引起心理恐慌,造成经济损失,甚至引起社会动荡[1]. 随着互联网的发展,信息在社交网络的传播已经成为常态,人们之间的信息交流变得更加频繁,这无疑加速了谣言的传播. 为了减少谣言带来的负面影响,促进社会健康发展,研究谣言的传播机制是十分有必要的.

    由于传播机制的相似性,大多数谣言的传播都是基于传染病模型,其中:文献[2]研究了遗忘机制对谣言传播的影响;文献[3]研究了犹豫机制对谣言传播的影响;文献[4]研究了不同态度对谣言传播的影响. 特别是随着翻译软件的发展和人们教育程度的提高,导致了多种语言的谣言传播[5-7]. 本文基于文献[7],考虑了在多语言环境下,人际传播与网络媒体传播相结合对谣言传播的影响.

1.   模型的建立
  • 假设将人群分为两组,第一组通过语言1传播谣言,第二组通过语言2传播谣言. 考虑到有人掌握多种语言或者会使用翻译软件,使得信息可以在不同的群体之间交换,因此谣言的交叉传播机制是存在的. 将每组分为4类:从未听过谣言,但易受谣言影响的人Si(t),传播谣言的人Ii(t),网络上的谣言信息量Mi(t)(Ii(t)在网络上产生的谣言信息量),知道谣言但不传播的人Ri(t),i=1,2. 建立模型如下:

    模型中所有的参数都是正的,并且A=(αij)2×2B=(βij)2×2是不可约的,ij=1,2. 在谣言传播过程中,易受谣言影响的人与传播谣言的人相遇后,可能产生传播谣言的兴趣,以βij的传播率变为传播谣言的人,βii表示SiIi的接触传播率,βij表示SiIj的交叉接触传播率(ij);易受谣言影响的人看到网络谣言信息后,可能产生传播谣言的兴趣,以αij的传播率变为传播谣言的人,αii表示SiMi的接触传播率,αij表示SiMj的交叉接触传播率(ij);传播谣言的人与知道谣言但不传播的人相遇后,可能失去传播谣言的兴趣,以φi的传播率变为知道谣言但不传播的人;Ai表示Si的输入率;μi表示自然死亡率;σi表示Ii的自然遗忘率;δi表示单位时间内一个谣言传播者在网络上产生的谣言量;di表示网络谣言信息的自然消除率.

2.   基本再生数
  • 模型(1)存在无谣言平衡点$\boldsymbol{E}^0=\left(S_1^0, 0, 0, 0, S_2^0, 0, 0, 0\right), S_i^0=\frac{A_i}{\mu_i}, i=1, 2 $. 模型(1)的可行域为$ X=\left\{S_1, I_1, M_1, R_1, S_2, I_2, M_2, R_2 \in \mathbb{R}_{+}^8 \mid S_i+I_i+R_i \leqslant \frac{A_i}{\mu_i}, 0 \leqslant M_i \leqslant \frac{A_i \delta_i}{\mu_i d_i}, i=1, 2\right\}$.

    根据下一代矩阵法[8],得到

    计算模型(1)的基本再生数为$R_0=\rho\left(\boldsymbol{F} \boldsymbol{V}^{-1}\right)=\rho\left(\frac{\left(\beta_{i j}+\alpha_{i j} \frac{\delta_j}{d_j}\right) S_i^0}{\mu_j+\sigma_j}\right)_{2 \times 2}, i, j=1, 2 $. 令$\boldsymbol{M}(\boldsymbol{S}^0)=\left(\frac{\left(\beta_{i j}+\alpha_{i j} \frac{\delta_j}{d_j}\right) S_i^0}{\mu_i+\sigma_i}\right)_{2 \times 2} $,其中$ \boldsymbol{S}^0=\left(S_1^0, S_2^0\right)^T$,易验证$R_0=\rho\left(\boldsymbol{M}\left(\boldsymbol{S}^0\right)\right) $.

3.   无谣言平衡点的稳定性
  • 定理1  当R0 < 1时,模型(1)的无谣言平衡点E0是局部渐近稳定的.

      为了证明无谣言平衡点E0是局部渐近稳定的,需要检验文献[8]中的假设(A1)-(A5). 假设(A1)-(A4)是显然成立的,条件(A5)需要证明下列8×8矩阵的全部特征根具有负实部.

    其中:$ \boldsymbol{W}=\boldsymbol{F}-\boldsymbol{V}=\left(\begin{array}{cccc} \beta_{11} S_1^0-\left(\mu_1+\sigma_1\right) & \beta_{12} S_1^0 & \alpha_{11} S_1^0 & \alpha_{12} S_1^0 \\ \beta_{21} S_2^0 & \beta_{22} S_2^0-\left(\mu_2+\sigma_2\right) & \alpha_{21} S_2^0 & \alpha_{22} S_2^0 \\ \delta_1 & 0 & -d_1 & 0 \\ 0 & \delta_2 & 0 & -d_2 \end{array}\right)$,

    定义s(W)为矩阵W的最大特征值,由文献[8]中的定理2可知下列等式成立:

    计算J4的特征根,可知$ \lambda_1=\lambda_2=-\mu_1<0, \lambda_3=\lambda_4=-\mu_2<0 \text {. 如果 } R_0<1 \text {, 则 } s(\boldsymbol{W})<0$,所以$s\left(\boldsymbol{J} \mid \boldsymbol{E}^0\right)<0 $,条件(A5)成立. 因此,当R0 < 1时,无谣言平衡点E0是局部渐近稳定的.

    定理2  当R0≤1时,模型(1)的无谣言平衡点E0是全局渐近稳定的.

      构造一个Lyapunov函数

    其中$\boldsymbol{I}(t)=\left(I_1(t), I_2(t)\right)^{\mathrm{T}}, \boldsymbol{w}^{\mathrm{T}}=\left(w_1, w_2\right), \boldsymbol{w}^{\mathrm{T}} \text { 为 } \boldsymbol{M}\left(\boldsymbol{S}^0\right) $关于特征值ρ(M(S0))的一个正的左特征向量,$S_i^0=\frac{A_i}{\mu_i}, i=1, 2 $.

    计算V0(t)沿着模型(1)解的全导数,

    $R_0 \leqslant 1 \text { 时, } \frac{\mathrm{d} V_0(t)}{\mathrm{d} t} \leqslant 0 \text {, 且 } \frac{\mathrm{d} V_0(t)}{\mathrm{d} t}=0 \text { 时, } I_i(t)=0, i=1, 2 $. 即当E0= $\left(\frac{A_1}{\mu_1}, 0, 0, 0, \frac{A_2}{\mu_2}, 0, 0, 0\right) \text { 时, } \frac{\mathrm{d} V_0(t)}{\mathrm{d} t}=0 \text {. 集合 }\left\{\left(S_1, I_1, M_1, R_1, S_2, I_2, M_2, R_2\right) \mid V_0^{\prime}(t)=0\right\} $的最大不变集为单点集{E0},根据LaSalle不变集原理[9],当R0≤1时,无谣言平衡点E0是全局渐近稳定的.

4.   谣言盛行平衡点的存在性及唯一性
  • 定理3  对于模型(1),R0>1时,模型是一致持续的,即存在一个正数ε,使得$\liminf _{t \rightarrow \infty}\left(I_i(t), M_i(t)\right) \geqslant(\varepsilon, \varepsilon), i=1, 2 $.

      定义

    首先,容易验证X0是正不变集. 根据模型(1)的有界性,可知模型(1)是点耗散的.

    $M_{\partial}=\left\{S_i, I_i, M_i, R_i \mid S_i, I_i, M_i, R_i \in \partial X_0, \forall t \geqslant 0, i=1, 2\right\} $.

    下面证明

    由于$ \left\{S_i(t), 0, 0, R_i(t) \mid S_i(t) \geqslant 0, R_i(t) \geqslant 0, i=1, 2\right\} \subseteq M_{\partial}$恒成立,所以只需证$M_{\partial} \subseteq\left\{S_i(t), 0, 0, R_i(t) \mid S_i(t) \geqslant 0, R_i(t) \geqslant 0, i=1, 2\right\} $即可. 假设不成立,那么存在i0,不失一般性,假设$i_0=2 \text {, 当 } t_0 \geqslant 0 \text { 时, } I_2\left(t_0\right)>0, M_2\left(t_0\right)=0 $,则有

    即存在正数τ,当$t_0<t<t_0+\tau \text { 时 } \frac{\mathrm{d} M_2}{\mathrm{~d} t}>0 \text {. 这说明 } t_0<t<t_0+\tau \text { 时, }\left(S_i(t), I_i(t), M_i(t)\right. \text {, }\left.R_i(t)\right) \notin M_a, i=1, 2 $. 这与假设矛盾,即证明了$ M_{\partial}=\left\{S_i(0), 0, 0, R_i(0) \mid S_i(t) \geqslant 0, R_i(t) \geqslant 0, i=1, 2\right\}$.

    下面证明$ W^S\left(\boldsymbol{E}^0\right) \cap X_0=\varnothing$,利用反证法,假设$W^S\left(\boldsymbol{E}^0\right) \cap X_0 \neq \varnothing $.

    由文献[10]可知,设ε1为任意充分小的正数,存在一个正常数η=η(ε1),使得

    由模型(1)可以得到

    考虑一个辅助系统

    系统(3)在平衡点的雅可比矩阵为

    其中$b_1=\beta_{11}\left(S_1^0-\varepsilon_1\right)-\varphi_1 \varepsilon_1-\left(\mu_1+\sigma_1\right), b_2=\beta_{22}\left(S_2^0-\varepsilon_1\right)-\varphi_2 \varepsilon_1-\left(\mu_2+\sigma_2\right) \text {, 由于 } R_0>1 \Leftrightarrow s(\boldsymbol{W})>0 $,则当R0>1时,存在充分小的正数$ \zeta\left(\zeta \geqslant \varepsilon_1\right) \text { 使得 } s\left(\boldsymbol{W}-\zeta \boldsymbol{W}_0\right)>0 \text {, 进而有 } s\left(\boldsymbol{W}_2\right)=s\left(\boldsymbol{W}-\varepsilon_1 \boldsymbol{W}_0\right) \geqslant s\left(\boldsymbol{W}-\zeta \boldsymbol{W}_0\right)>0$,其中

    这说明矩阵W2至少有一个正的特征根. 因此系统(3)是不稳定的,则系统(3)的解满足下列式子

    通过比较定理可得

    这与$ I_i(t)<\varepsilon, M_i(t)<\varepsilon \text { 矛盾, 因此 } W^S\left(\boldsymbol{E}^0\right) \cap X_0=\varnothing \text { 成立. 集合 } M_{\partial}$内的每条轨线都收敛到E0,且E0是非周期的. 根据文献[11]可知,模型(1)关于$\left(X_0, \partial X_0\right) $是一致持续的. 结合文献[12]可知,模型(1)至少存在一个谣言盛行平衡点$\boldsymbol{E}^*=\left(S_i^*, I_i^*, M_i^*, R_i^*\right), i=1, 2 $.

    定理4  当R0>1时,模型(1)存在唯一的一个谣言盛行平衡点$\boldsymbol{E}^*=\left(S_i^*, I_i^*, M_i^*, R_i^*\right), i=1, 2 $.

      首先,计算模型(1)的谣言盛行平衡点

    得到$S_i=\frac{A_i}{\mu_i}-I_i-\frac{\sigma_i I_i}{\mu_i-\varphi_i I_i}, M_i=\frac{\delta_i I_i}{d_i}, R_i=\frac{\sigma_i I_i}{\mu_i-\varphi_i I_i}, i=1, 2 $.

    模型(1)的平衡点等价于

    根据文献[13]的方法,假设$I_i^*=h>0, I_i^*=k>0 $是模型的两个常数解,如果hk,那么至少存在一个i(i=1,2),使得hiki. 不失一般性,假设$h_1>k_1 \text {, 进一步可设 } \frac{h_1}{k_1} \geqslant \frac{h_i}{k_i} $对任意的i(i=1,2)成立. 将hk带入(4)式得

    进行变换得到

    对任意的i(i=1,2)有$k_i \geqslant k_1\left(\frac{h_i}{h_1}\right) $成立,且

    进一步得到

    这与(6)式矛盾,因此模型(1)存在唯一的谣言盛行平衡点$\boldsymbol{E}^*=\left(S_i^*, I_i^*, M_i^*, R_i^*\right), i=1, 2 $.

5.   谣言盛行平衡点的稳定性
  • 定理5  当R0>1时,模型(1)的谣言盛行平衡点E*是全局渐近稳定的.

      令$J_1(t)=\left(S_i-S_i^*-S_i^* \ln \frac{S_i}{S_i^*}\right)+\left(I_i-I_i^*-I_i^* \ln \frac{I_i}{I_i^*}\right)+\frac{\varphi_i R_i^*}{\varphi_i R_i^*+\sigma_i}\left(R_i-R_i^*-R_i^* \ln \frac{R_i}{R_i^*}\right), $$ J_2(t)=\sum\limits_{j=1}^2 \alpha_{i j} S_i^* M_j^* \frac{1}{\delta_j I_j^*}\left(M_j-M_j^*-M_j^* \ln \frac{M_j}{M_j^*}\right), i=1, 2 \text {. 令 } f(x)=1-x+\ln x \text {, 则对 } \forall x>0 \text {, }$f(x)≤0,当且仅当x=1时,f(x)=0.

    J1(t),J2(t)沿着模型(1)解的全导数分别为

    构造Lyapunov函数

    V(t)沿着模型(1)解的全导数为

    由于A=(αij)2×2B=(βij)2×2是不可约的,根据文献[14]中的定理2.3,有$\sum\limits_{i, j=1}^2 v_i\left(\beta_{i j} S_i^* I_j^*+\right. $$\left.\alpha_{i j} S_i^* M_j^*\right)\left(\frac{I_j}{I_j^*}-\ln \frac{I_j}{I_j^*}\right)=\sum\limits_{i, j=1}^2 v_i\left(\beta_{i j} S_i^* I_j^*+\alpha_{i j} S_i^* M_j^*\right)\left(\frac{I_i}{I_i^*}-\ln \frac{I_i}{I_i^*}\right) \text {, 于是得到 } \frac{\mathrm{d} V(t)}{\mathrm{d} t} \leqslant 0 $,当且仅当$\boldsymbol{E}^*=\left(S_i^*, I_i^*, M_i^*, R_i^*\right), i=1, 2 \text { 时, } \frac{\mathrm{d} V(t)}{\mathrm{d} t}=0 \text {. 那么, } V^{\prime}(t)=0 $的最大不变集为单点集{E*},根据LaSalle不变集原理[9]可知,当R0>1时,谣言盛行平衡点E*是全局渐近稳定的.

6.   结论
  • 本文基于谣言传播的若干特点,考虑个体和媒介交叉传播这一风险因素,建立SIMR模型来研究交叉传播的影响. 首先,通过下一代矩阵的方法定义基本再生数R0;其次,构造Lyapunov函数证明了当基本再生数R0≤1时,模型的无谣言平衡点是全局渐近稳定的;然后根据持续性理论,证明了当基本再生数R0>1时,模型是一致持续的,且存在唯一的谣言盛行平衡点;最后,利用Lyapunov函数和图论知识,证明了谣言盛行平衡点是全局渐近稳定的. 结果说明交叉传播会影响模型的基本再生数,但不会影响谣言传播的动力学属性. 该研究丰富了谣言传播动力学的研究方法,拓展了人们对谣言传播动力学的认识,有益于谣言控制措施的制定.

Reference (14)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return