%0 Journal Article
%T 具有自我学习机制的网络谣言传播与仿真研究<br>Propagation and Simulation Research of Network Rumors with Self-Learning Mechanism
%A 马宇红
%A 张琴
%A 陈闪&lt
%A br&gt
%A MA Yu-hong
%A ZHANG Qin
%A CHEN Shan
%J 西南大学学报(自然科学版)
%D 2017
%R 10.13718/j.cnki.xdzk.2017.05.027
%X 将社交网络中的个体设为健康者(S)、传播者(I)、反击者(C)和免疫者(R) 4种状态，根据不同状态之间的转移机制建立了SICR谣言传播模型.针对“人云亦云”的社会从众心理，引入个体的自我学习机制，基于BA无标度网络仿真分析了自我学习机制以及初始传播者、天然反击者重要性对谣言传播行为的影响.结果显示：自我学习机制能够促进谣言传播；初始传播者越重要，谣言传播范围越广、速度越快；天然反击者的重要性越高，抑制谣言传播的效果越明显.<br>The individuals in social networks are divided into four states: susceptible (S), infective (I), counterattack (C) and refractory (R), a kind of transition rule between different states is introduced, and then a new SICR rumor propagation model is established. Based on the social conformity behavior of 'follow the herd', this paper introduces a self-learning mechanism in the process of rumor propagation. The effects of self-learning mechanism and the importance of initial infective or counterattack on rumor diffusion are simulated and analyzed based on BA free-scale networks. The results show that self-learning mechanism can promote rumor diffusion; the more important the initial infective is, the wider the spreading range of the rumor will be; and the more important the initial counterattack is, the better the effect of inhibiting rumor diffusion will be
%K 社交网络
%K 谣言传播
%K 自我学习机制
%K SICR谣言传播模型
%K 动态转移概率&lt
%K br&gt
%K social network
%K rumor propagation
%K self-learning mechanism
%K SICR rumor-spreading model
%K dynamic transition probability
%U http://xbgjxt.swu.edu.cn/jsuns/html/jsuns/2017/5/201705027.htm