Rumor-Propagation Model with Consideration of Refutation Mechanism in Homogeneous Social Networks

被引:17
|
作者
Zhao, Laijun [1 ,2 ]
Wang, Xiaoli [3 ]
Wang, Jiajia [1 ,2 ]
Qiu, Xiaoyan [4 ]
Xie, Wanlin [4 ]
机构
[1] Shanghai Jiao Tong Univ, Sino US Global Logist Inst, Shanghai 200030, Peoples R China
[2] Shanghai Jiao Tong Univ, Antai Coll Econ & Management, Shanghai 200052, Peoples R China
[3] Shanghai Univ Engn Sci, Sch Management, Shanghai 201620, Peoples R China
[4] Shanghai Univ, Sch Management, Shanghai 200444, Peoples R China
基金
中国国家自然科学基金;
关键词
SPREADING MODEL;
D O I
10.1155/2014/659273
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
In recent years, increasing attention has been paid to how to effectively manage rumor propagation. Based on previous studies of rumor propagation and some strategies used by the authorities to refute rumors and manage rumor propagation, we develop a new rumor-propagation model with consideration of refutation mechanism. In this paper, we describe the dynamic process of rumor propagation by accounting for the refutation mechanism in homogeneous social networks. And then, we derive mean-field equations for rumor-propagation process. We then analyze the stability of the model with respect to changes in parameter values. Our results show that there exists a critical threshold lambda(c) that is inversely proportional to the average degree of the social networks and is positively correlated with the strength of the refutation mechanism. If the spreading rate is bigger than the critical threshold lambda(c), rumors can be spread. Our numerical simulations in homogeneous networks demonstrate that increasing the ignorant's refutation rate beta can reduce the peak value of spreaders density, which is better than increasing the spreader's refutation rate eta. Therefore, based on the seriousness of the rumor propagation and the rumor-propagation rate, the authorities can choose effective strategies that increase the refutation rate so that they can reduce the maximum influence of the rumor.
引用
收藏
页数:11
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