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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (10): 53-63    DOI: 10.11925/infotech.2096-3467.2017.0503
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Simulating Public Opinion Evolution with Scale-Free Network Model and Infectious Disease Model
Han Pu1,2(), Wang Peng1
1 School of Management, Nanjing University of Posts & Telecommunications, Nanjing 210003, China
2 Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
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[Objective] This article tries to explore the information dissemination status and process in the social network systems, aiming to reveal the online information evolution mechanism. [Methods] First, we added adjustable parameters to the scale-free network model and the infectious disease model. Then, we executed the modified model on the NetLogo platform to simulate the evolution of public opinion. [Results] We found that the changing propagation rate was a better way to describe the online information dissemination process. We could effectively guide and control the information flow in a large network at the stage with increasing propagation rate. [Limitations] We need better classification method for the target population. [Conclusions] The proposed model could simulate information evolution and then support the online public opinion monitoring, guidance and control.

Key wordsScale-Free Network      Infectious Disease Model      Public Opinion Communication     
Received: 31 May 2017      Published: 08 November 2017
ZTFLH:  TP311 G350  

Cite this article:

Han Pu,Wang Peng. Simulating Public Opinion Evolution with Scale-Free Network Model and Infectious Disease Model. Data Analysis and Knowledge Discovery, 2017, 1(10): 53-63.

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微分方程 实际意义 NetLogo中表示
k 潜在传播者与传播者接触, 接受并传播信息的概率 virus-spread-chance
λ 网络模型中节点的度
μ 传播者变为抵触者和潜在传播者的概率 recovery-chance
φ 传播者转变为抵触者的概率 gain-resistant-chance
参数 网络A 网络B
初始孤立节点个数 10 8
目标网络节点个数 1 000 2 000
初始化网络选择连接点数 10 8
新加入节点时与网络中连接的节点个数 3 10
初始节点个数 3 2
重新连接边数 4 10
Gain-resistant-chance(变为抵触者概率) 8% 8%
Virus-spread-chance(传播概率) 2% 2%
3% 3%
[1] Barabasi A, Albert R.Emergence of Scaling in Random Networks[J]. Science, 1999, 286(5439): 509-512.
doi: 10.1126/science.286.5439.509 pmid: 10521342
[2] Watts D J, Strogatz S H.Collective Dynamics of ‘Small- World’ Networks[J]. Nature, 1998, 393(6684): 440-442.
doi: 10.1038/30918
[3] Chen P Y, Chen K C.Information Epidemics in Complex Networks with Opportunistic Links and Dynamic Topology[C]// Proceedings of the 2010 IEEE Global Telecommunications Conference (GLOBECOM). 2010: 1-6.
[4] 宋楠, 付举磊, 鲍勤, 等. 基于无标度网络的恐怖信息传播与最优应对策略[J]. 系统工程理论与实践, 2015, 35(3): 630-640.
[4] (Song Nan, Fu Julei, Bao Qin, et al.Cyber Terrorism Spreading and Optimal Intervention Policies Based on a Scale-free Network[J]. Systems Engineering—Theory & Practice, 2015, 35(3): 630-640.)
[5] 沈贤. 基于无标度网络的谣言传播模型与控制策略的研究[D]. 南京: 南京邮电大学, 2013.
[5] (Shen Xian.The Propagation and Control Strategies of Rumors in Scale-free Network[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2013. )
[6] 余海林, 庄亚明. 具有无标度特性的群体性突发事件信息传播网络模型及仿真[J]. 情报科学, 2015, 32(11): 90-94.
[6] (Yu Hailin, Zhuang Yaming.Modeling and Simulating of the Information Propagation Network of Mass Unexpected Incidents with Scale-free Characteristic[J]. Information Science, 2015, 32(11): 90-94.)
[7] 易成岐. 社会网络的信息传播机制及控制方法研究[D]. 哈尔滨: 哈尔滨理工大学, 2016.
[7] (Yi Chengqi.Research on Mechanisms of Information Propagation and Control Strategies in Social Networks[D]. Harbin: Harbin University of Science and Technology, 2016.)
[8] 黄宏程, 蒋艾玲, 胡敏. 基于社交网络的信息传播模型分析[J]. 计算机应用研究, 2016, 33(9): 2738-2742.
doi: 10.3969/j.issn.1001-3695.2016.09.040
[8] (Huang Hongcheng, Jiang Ailing, Hu Min.Analysis of Information Diffusion Model on Social Network[J]. Application Research of Computers, 2016, 33(9): 2738-2742.)
doi: 10.3969/j.issn.1001-3695.2016.09.040
[9] 徐翔斌, 李恒, 王坤. Web2.0网络信息传播影响机制研究[J]. 情报科学, 2015, 33(8): 44-49.
[9] (Xu Xiangbin, Li Heng, Wang Kun.Research on the Influence Mechanism of Information Spreading in Web2.0 Network[J]. Information Science, 2015, 33(8): 44-49.)
[10] 孙立晟, 何东之. 改进无标度网络模型研究[J]. 电子设计工程, 2016, 24(6): 115-117.
[10] (Sun Lisheng, He Dongzhi.Research on Improved Scale-free Networks Model[J]. International Electronic Elements, 2016, 24(6): 115-117.)
[11] 李涛, 刘启明. 基于无标度网络的谣言传播模型研究[J]. 河北师范大学学报, 2016, 40(3): 195-199.
[11] (Li Tao, Liu Qiming.A Study of Rumors Spreading Model with on Scale-free Network[J]. Journal of Hebei Normal University, 2016, 40(3): 195-199.)
[12] 姜启源, 谢金星, 叶俊. 数学模型[M]. 第4版. 北京: 高等教育出版社, 2011.
[12] (Jiang Qiyuan, Xie Jinxing, Ye Jun.Mathematical Model [M]. The 4th Edition. Beijing: Higher Education Press, 2011.)
[13] 魏静, 朱恒民, 宋瑞晓, 等. 个体视角下的网络舆情传递链路预测分析[J]. 现代图书情报技术, 2016(1): 55-64.
[13] (Wei Jing, Zhu Hengmin, Song Ruixiao, et al.Link Prediction Analysis of Internet Public Opinion Transfer from the Individual Perspective[J]. New Technology of Library and Information Service, 2016(1): 55-64.)
[14] 朱恒民, 刘凯, 卢子芳. 媒体作用下互联网舆情话题传播模型研究[J]. 现代图书情报技术, 2013(3): 45-50.
[14] (Zhu Hengmin, Liu Kai, Lu Zifang.Study on Topic Propagation Model of Internet Public Opinion Under the Influence of the Media[J]. New Technology of Library and Information Service, 2013(3): 45-50.)
[15] 马知恩. 传染病动力学的数学建模与研究[M]. 北京: 科学出版社, 2004.
[15] (Ma Zhien.Mathematical Modeling and Research on Dynamics of Infectious Diseases[M]. Beijing: Science Press, 2004. )
[16] 沈乾, 马宁, 黄远, 等. 微博传播波的函数解析实证研究[J]. 数学的实践与认识, 2014, 44(21): 143-151.
[16] (Shen Qian, Ma Ning, Huang Yuan, et al.Empirical Study on Microblog Forwarding Wave Based on Functional Analysis[J]. Mathematics in Practice and Theory, 2014, 44(21): 143-151.)
[17] 刘咏梅, 彭琳, 赵振军. 基于小世界网络的微博谣言传播演进研究[J]. 复杂系统与复杂性科学, 2014, 11(4): 54-60.
doi: 10.13306/j.1672-3813.2014.04.010
[17] (Liu Yongmei, Peng Lin, Zhao Zhenjun.The Evolution of Rumor Spread on Micrblog Based on Small-World Network[J]. Complex Systems and Complexity Science, 2014, 11(4): 54-60.)
doi: 10.13306/j.1672-3813.2014.04.010
[1] Yang Liu, Zhu Hengmin, Ma Jing. Evolution Model of Microblog Public Opinion Considering the Influence of Next-nearest Neighbors[J]. 现代图书情报技术, 2014, 30(12): 78-84.
[2] Chen Nan, Wang Hengshan. A Research About the Best Time Points for the Government to Intervene Network Public Opinion[J]. 现代图书情报技术, 2012, 28(3): 53-58.
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