考虑加权传播率和随机干扰的谣言传播模型
A Rumor Propagation Model Considering Weighted Propagation Rate and Stochastic Noise
DOI: 10.12677/mos.2024.135497, PDF,    国家自然科学基金支持
作者: 徐文龙:南京邮电大学理学院,江苏 南京;王友国*:南京邮电大学理学院,江苏 南京;南京邮电大学通信与信息工程学院,江苏 南京;翟其清:南京邮电大学通信与信息工程学院,江苏 南京
关键词: 社交网络SIR谣言传播模型随机噪声加权传播率蒙特卡洛仿真Social Networks SIR Rumor Propagation Model Stochastic Noise Weighted Propagation Rate Monte Carlo Simulation
摘要: 在线社交网络已经成为互联网信息时代的信息传播的主要途径,对社交网络中谣言的性质、特征以及信息传播的研究一直是一项重要的研究课题。考虑社交网络环境及用户都会受到不确定因素的影响,网络结构和用户数量都会随时间发生变化,因此,在谣言传播过程中引入随机噪声;同时针对个体间的差异性,以及不同个体间连接紧密程度不同,引入加权传播率,基于此建立考虑加权传播率和随机干扰的谣言传播模型,并对此模型进行了传播动力学研究。推导了全局正解存在唯一性,推导了谣言消亡条件;并通过蒙特卡洛方法在BBV网络(加权无标度网络)以及LW网络(局域世界演化网络)上进行了仿真,验证了理论推导的合理性。实验结果表明,局域世界特性会加速谣言传播,扩大谣言最终规模;传播率的差异性能够抑制谣言峰值和最终规模;在保证网络拓扑结构不出现剧烈变化的情况下,弱噪声的加入会加速谣言的传播。
Abstract: Online social networks have become the primary channel for information dissemination in the Internet information age, making the study of the nature and characteristics of rumors in social networks, as well as the research on information propagation, an important subject of research. Considering that the social network environment and users are subject to the influence of uncertain factors, and that network structure and user numbers change over time, Stochastic noise is introduced into the rumor propagation process. At the same time, to account for the individual differences and the varying degrees of connection tightness between different individuals, a weighted transmission rate is introduced. Based on this, a rumor propagation model considering the weighted transmission rate and stochastic noise is established, and the dynamics of propagation for this model are studied. The uniqueness of the existence of the global positive solution is deduced, and the conditions for the demise of rumors are deduced. The Monte Carlo method is used to simulate the BBV network (weighted scale-free network) and LW network (local world evolution network), and the rationality of the theoretical derivation is verified. The experimental results show that the local world characteristics will accelerate the spread of rumors and expand the final scale of rumors. The difference in transmission rate can suppress the peak and final scale of rumors. In the case of ensuring that the network topology does not change drastically, the addition of weak noise will accelerate the spread of rumors.
文章引用:徐文龙, 王友国, 翟其清. 考虑加权传播率和随机干扰的谣言传播模型[J]. 建模与仿真, 2024, 13(5): 5491-5502. https://doi.org/10.12677/mos.2024.135497

参考文献

[1] 中国互联网络信息中心. 第51次中国互联网络发展状况统计报告[EB/OL].
https://www.cnnic.cn/n4/2023/0303/c88-10757.html, 2023-03-02.
[2] 王旭丽, 吴恋, 刘然, 等. 网络谣言的传播机制研究[J]. 电脑知识与技术: 学术版, 2019(2Z): 48-49.
[3] Lazer, D.M.J., Baum, M.A., Benkler, Y., Berinsky, A.J., Greenhill, K.M., Menczer, F., et al. (2018) The Science of Fake News. Science, 359, 1094-1096. [Google Scholar] [CrossRef] [PubMed]
[4] 仓林青. 复杂社交网络上Si-SIR谣言传播模型的建模与研究[D]: [硕士学位论文]. 南京: 南京邮电大学, 2022.
[5] Tulu, M.M., Hou, R. and Younas, T. (2018) Identifying Influential Nodes Based on Community Structure to Speed up the Dissemination of Information in Complex Network. IEEE Access, 6, 7390-7401. [Google Scholar] [CrossRef
[6] Yang, D., Chow, T.W.S., Zhong, L., Tian, Z., Zhang, Q. and Chen, G. (2018) True and Fake Information Spreading over the Facebook. Physica A: Statistical Mechanics and Its Applications, 505, 984-994. [Google Scholar] [CrossRef
[7] Yi, Y., Zhang, Z. and Gan, C. (2018) The Effect of Social Tie on Information Diffusion in Complex Networks. Physica A: Statistical Mechanics and its Applications, 509, 783-794. [Google Scholar] [CrossRef
[8] Liu, X., He, D., Yang, L. and Liu, C. (2019) A Novel Negative Feedback Information Dissemination Model Based on Online Social Network. Physica A: Statistical Mechanics and Its Applications, 513, 371-389. [Google Scholar] [CrossRef
[9] Melbourne, B.A. and Hastings, A. (2008) Extinction Risk Depends Strongly on Factors Contributing to Stochasticity. Nature, 454, 100-103. [Google Scholar] [CrossRef] [PubMed]
[10] 王家坤, 王新华. 一种基于线性阈值的网络谣言离散传播模型[J]. 情报科学, 2019, 37(6): 163-169.
[11] 朱亮. 随机扰动下的网络信息传播模型及溯源模型研究[D]: [博士学位论文]. 南京: 南京邮电大学, 2020.
[12] 鲜佳君. 复杂网络上的信息传播及其干预策略研究[D]: [博士学位论文]. 成都: 电子科技大学, 2020.
[13] 孙玺菁, 司守奎. 复杂网络算法与应用[M]. 北京: 国防工业出版社, 2015.
[14] 苏凯, 汪李峰, 张卓. 一种灵活的加权复杂网络演化模型及其仿真[J]. 系统仿真学报, 2009, 21(1): 266-271.
[15] Barrat, A., Barthélemy, M. and Vespignani, A. (2004) Weighted Evolving Networks: Coupling Topology and Weight Dynamics. Physical Review Letters, 92, Article ID: 228701. [Google Scholar] [CrossRef] [PubMed]
[16] Sen, Q. and Guan-Zhong, D. (2009) A New Local-World Evolving Network Model. Chinese Physics B, 18, 383-390. [Google Scholar] [CrossRef
[17] Newman, M.E.J. (2004) Analysis of Weighted Networks. Physical Review E, 70, Article ID: 056131. [Google Scholar] [CrossRef] [PubMed]
[18] 刘继学. 加权网络模型的病毒传播与免疫[D]: [硕士学位论文]. 桂林: 广西师范大学, 2011.
[19] 张芹, 蒋国平, 宋波等. 具有社团结构的加权网络的病毒传播研究[J]. 计算机技术与发展, 2015, 25(1): 151-154.
[20] Shaikhet, L. (2020) Stability of Equilibria of Rumor Spreading Model under Stochastic Perturbations. Axioms, 9, Article No. 24. [Google Scholar] [CrossRef
[21] Jain, A., Dhar, J. and Gupta, V. (2019) Stochastic Model of Rumor Propagation Dynamics on Homogeneous Social Network with Expert Interaction and Fluctuations in Contact Transmissions. Physica A: Statistical Mechanics and Its Applications, 519, 227-236. [Google Scholar] [CrossRef
[22] Zhu, L. and Wang, Y. (2017) Rumor Spreading Model with Noise Interference in Complex Social Networks. Physica A: Statistical Mechanics and Its Applications, 469, 750-760. [Google Scholar] [CrossRef
[23] Chai, Y., Wang, Y. and Zhu, L. (2019) A Stochastic Information Diffusion Model in Complex Social Networks. IEEE Access, 7, 175897-175906. [Google Scholar] [CrossRef
[24] 姚尊强, 尚可可, 许小可. 加权网络的常用统计量[J]. 上海理工大学学报, 2012, 34(1): 18-26.
[25] 厄克森达尔. 随机微分方程导论与应用[M]. 第6版. 刘金山, 吴付科, 译. 北京: 科学出版社, 2017: 5-67.