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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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Abstract  

[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.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.0503     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I10/53

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%
Recovery-chance(传播者变为抵触者和潜在传
播者的概率)
3% 3%
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