有标记网络中的病毒传播机理及动态特性

doi:10.13190/j.jbupt.2020-145

北京邮电大学学报 ›› 2021, Vol. 44 ›› Issue (3): 73-78.doi: 10.13190/j.jbupt.2020-145

有标记网络中的病毒传播机理及动态特性

卓新建, 王文璇, 李慧嘉

北京邮电大学理学院, 北京 100876

收稿日期:2020-09-30 出版日期:2021-06-28 发布日期:2021-06-23
通讯作者: 李慧嘉(1985-),男,教授,E-mail:hjli@bupt.edu.cn. E-mail:hjli@bupt.edu.cn
作者简介:卓新建(1971-),男,副教授.
基金资助:
北京邮电大学提升科技创新能力行动计划项目（2020XD-A01-1）；国家自然科学基金项目（71871233）；北京市自然科学基金项目（1202020）

The Epidemic Spreading Mechanism and Dynamic Characteristics on Signatured Networks

ZHUO Xin-jian, WANG Wen-xuan, LI Hui-jia

School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2020-09-30 Online:2021-06-28 Published:2021-06-23

摘要/Abstract

摘要： 大数据环境下传播行为及动态演化形式是多元化的，其模型构建也各不相同.因此针对存在不同性质个体的有标记网络，探索了病毒传播模型与动态演化机制.首先将改进的有标记-易感染-已感染-易感染（S-SIS）传播模型应用到有标记网络，通过调节参数研究其对病毒传播的影响.改进了传统模型中传染概率为常数的假设，更好地模拟了感染状态的动态演化，构建了感染状态实时变化的感染概率方程，准确模拟了有标记网络中的病毒传播行为.最后利用平均场理论和蒙特卡洛方法进行仿真，对比检验不同参数变化对模型的影响.为了观察具有不同拓扑结构网络上的表现，对埃尔德什-雷尼随机网络与无标度网络进行了深入实验，得出了一系列新颖的结论.

关键词: 有标记网络, 易感染-已感染-易感染模型, 动态演化机制, 蒙特卡洛模拟, 平均场模型仿真

Abstract: Spreading behavior and its dynamic evolution in the context of big data are diversified, and thousands of information dissemination models have been developed. In this paper, aiming at the signatured network model with multi-types individuals, the spreading behavior and dynamical properties were explored. Firstly, the improved signatured-susceptible-infected-susceptible (S-SIS) model is applied to the signatured network, and then in order to simulate the spreading dynamical process. Specially, and simulate the dynamic evolution of the infection, the assumption in the existing model that the infection probability is constant is improved, an infection probability equation that changes in real time is constructed, and the spreading behavior in the signatured network is accurately simulated. Finally, mean filed analysis and Monte Carlo simulation is made to get the simulation result of the model. By adjusting the parameters and comparing the results, useful conclusions can be drawn. In order to observe the performance of networks with different topologies, Erdos-Rainey random network and scale-free network are simulated to observe the characteristic with different topology of networks, and the difference is observed in the simulation results.

Key words: signatured network, susceptible-infected-susceptible model, dynamic evolution process, Monte Carlo algorithm, mean filed analysis

中图分类号:

TN911.22

卓新建, 王文璇, 李慧嘉. 有标记网络中的病毒传播机理及动态特性[J]. 北京邮电大学学报, 2021, 44(3): 73-78.

ZHUO Xin-jian, WANG Wen-xuan, LI Hui-jia. The Epidemic Spreading Mechanism and Dynamic Characteristics on Signatured Networks[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(3): 73-78.

参考文献

[1] 郭世泽,陆哲明.复杂网络基础理论[M].北京:科学出版社,2012:120-135.
[2] Preciado V M, Zargham M, Enyioha C, et al. Optimal resource allocation for network protection against spread-ing processes[J]. IEEE Transactions on Control of Network Systems, 2014, 1(1):99-108.
[3] Pastor-Satorras R, Castellano C, Van Mieghem P, et al. Epidemic processes in complex networks[J]. Reviews of Modern Physics, 2015, 87(3):925-979.
[4] Kimura M, Saito K, Motoda H. Efficient estimation of influence functions for SIS model on social networks[C]//IJCAI-09 Conference. Pasadena:IJCAI Press, 2009:2046-2051.
[5] Paarporn K, Eksin C, Weitz J S, et al. Networked SIS epidemics with awareness[J]. IEEE Transactions on Computational Social Systems, 2017, 4(3):93-103.
[6] Xiao Yunpeng, Song Chenguang, Liu Yanbing. Social hotspot propagation dynamics model based on multidimensional attributes and evolutionary games[J]. Communications in Nonlinear Science and Numerical Simulation,2019,67:13-25.
[7] Ye Jihang, Cheng Hong, Zhu Zhe, et al. Predicting posi-tive and negative links in signed social networks by transfer learning[C]//22nd International Conference on World Wide Web. New York:Association for Computing Machinery Press, 2013:1477-1488.
[8] Jacob C, Viet A F. Epidemiological modeling in a branching population:particular case of a general SIS model with two age classes[J]. Mathematical Biosciences, 2003, 182(1):93-111.
[9] Kessler D A. Epidemic size in the SIS model of endemic infections[J]. Journal of Applied Probability, 2008, 45(3):757-778.
[10] Shi Hongjing, Duan Zhisheng, Chen Guanrong. An SIS model with infective medium on complex networks[J]. Physica A:Statistical Mechanics and Its Applications, 2008, 387(8-9):2133-2144.
[11] Leskovec J, Huttenlocher D, Kleinberg J. Signed networks in social media[C]//SIGCHI Conference on Human Factors in Computing Systems. New York:Association for Computing Machinery Press, 2010:1361-1370.
[12] Tang J, Chang Y, Aggarwal C, et al. A survey of signed network mining in social media[J]. ACM Computing Surveys (CSUR), 2016, 49(3):1-37.
[13] Van Mieghem P. The N-intertwined SIS epidemic network model[J]. Computing, 2011, 93(2-4):147-169.
[14] Ganesh A, Massoulié L, Towsley D. The effect of network topology on the spread of epidemics[C]//IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Miami:IEEE Press, 2005:1455-1466.
[15] Van Mieghem P. Performance analysis of communications networks and systems[M]. London:Cambridge University Press, 2009:179-228.

有标记网络中的病毒传播机理及动态特性

The Epidemic Spreading Mechanism and Dynamic Characteristics on Signatured Networks

RichHTML

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	袁建国余林峰游薇庞宇. 一种优化错误模式集的极化码 SCL-Flip 译码算法[J]. 北京邮电大学学报, 2023, 46(4): 76-82.
[2]	田心记王坤. IRS辅助下行NOMA系统中最大化折中的方法[J]. 北京邮电大学学报, 0, (): 0-0.
[3]	黄龙杨张楠刘笑笑申滨. 异构蜂窝网络中基于雾节点协作贡献度的计算卸载算法[J]. 北京邮电大学学报, 2023, 46(2): 37-42.
[4]	张浩冯春燕杨佳汇郭彩丽周博文. 面向语义通信的3D骨骼点数据编码与压缩方法[J]. 北京邮电大学学报, 2022, 45(6): 62-69.
[5]	王则予张梦菲孙耀华彭木根. 星上透明转发非地面网络中的切换机制研究[J]. 北京邮电大学学报, 2022, 45(6): 105-112.
[6]	朱剑锋孙耀华彭木根. 低轨卫星通信系统的前导序列设计研究[J]. 北京邮电大学学报, 2022, 45(6): 78-84.
[7]	牛凯吴泊霖戴金晟王森袁弋非. 面向6G系统的极化编码调制[J]. 北京邮电大学学报, 2022, 45(6): 1-11.
[8]	张顺外夏子寒. 基于SWIPT的乘积Polar编码协作构造与分析[J]. 北京邮电大学学报, 2022, 45(5): 54-59.
[9]	赵海英解光鹏高子惠. 基于数学规则的龟背纹自动生成方法[J]. 北京邮电大学学报, 2022, 45(4): 119-125.
[10]	赵海英, 解光鹏, 高子惠. 基于数学规则的龟背纹自动生成方法[J]. 北京邮电大学学报, 2022, 45(4): 110-115.
[11]	郭世泽, 吕仁健, 何明枢, 张杰, 俞赛赛. 流谱理论及其在网络防御中的应用[J]. 北京邮电大学学报, 2022, 45(3): 19-25.
[12]	王惠琴, 杨丽荣, 彭清斌, 曹明华, 陈丹. 基于离散小波变换的O-OFDM-DIM[J]. 北京邮电大学学报, 2022, 45(3): 57-63.
[13]	刘小明, 齐涛, 甘露. 一种宽带反射型极化旋转器[J]. 北京邮电大学学报, 2021, 44(6): 20-25,32.
[14]	刘绍华, 李明豪, 李兆歆, 毛天露, 刘京. 一种联合采样的神经网络光场[J]. 北京邮电大学学报, 2021, 44(6): 109-115.
[15]	赵海英, 李焕洪, 侯小刚. 基于感知驱动的纹饰矢量图评价方法[J]. 北京邮电大学学报, 2021, 44(6): 53-58.