【摘要】 当前网络谣言控制策略的研究大多考虑高连接度对消息传播的影响,即节点自身邻居数,忽略了其邻居信息产生的间接影响。为此,提出一种SDND谣言免疫策略,该策略只需了解网络局部信息,在选取免疫节点时综合考虑节点自身出度及其邻居最大出度。在新浪微博数据集上,借助SEIR谣言传播模型仿真谣言传播,对比分析目标免疫、熟人免疫、重要熟人免疫、SDND免疫对谣言传播的影响。仿真结果表明,SDND免疫效果优于目标免疫、熟人免疫等策略,能够较好地抑制谣言传播。更多还原

【Abstract】 Current researches concerning network rumor control strategy mainly focus on the effect of high connectivity on information propagation,namely,the number of neighbor nodes,and pay less attention to the indirect effects induced by neighbor information.Therefore,this paper proposes a Self-degree and Neighbor-degree(SDND) rumor immunization strategy,which merely needs to knowthe local information of network.In this strategy,immunization node is selected by comprehensively considering on the out-degree of the node and the maximum out-degree of the neighbor nodes.Subsequently,based on the dataset of Sina microblog,the effects of target immunization,acquaintance immunization,important acquaintance immunization,and SDND immunization on rumor propagation are compared by simulating rumor propagation using the SEIR rumor propagation model.Simulation results showthat SDND presents a superior immune effect than target immunization and acquaintance immunization strategies.It can effectively inhibit rumor propagation.更多还原