面向时间感知的微博传播模型研究

Research on Temporal Perception-oriented Microblog Propagation Model

随着在线社交网络的快速发展,提取信息传播特征和构建传播模型已成为研究热点。针对传统微博网络传播模型未考虑用户的不完整阅读、潜伏期和直接免疫等行为难以准确识别免疫节点的缺陷,通过分析用户的行为特征,提出增加传播的个体分类和完善传播途径的微博网络传播算法(MSILR)。该算法可以使用户根据微博信息的传播特性及时获得、传播和屏蔽信息,根据模型反映的社会关系和在线社会行为完善社交网络的功能。以新浪微博为例,分析微博网络的传播机理和网络参数对信息传播过程的影响,完善动力学演化方程组,刻画信息传播过程随时间的演化规律。在真实微博网络数据集上进行测试,并与传统算法进行比较,实验结果表明了MSILR算法的有效性和可行性。

With the rapid development of online social networks,extracting information propagation characteristics and building the propagation model have become hot research topics.The traditional propagation models of the microblog network have not considered the users＇ incomplete reading behavior,the incubation period and direct immunization at the same time,so they couldn＇t accurately identify the immune nodes.In view of these defects,by analyzing the behavior characteristics of the users,the paper proposed a MSLIR which increases the classification of spread individuals and improves the propagation path.The users can get,spread and shield information timely according to the propagation characteristics of microblog information by the algorithm,the function of social network will be improved according to the model response of social relations and online social behaviors.Taking sina microblog as an example,the paper analyzed the effect of the propagation mechanism and network parameters on the processes of information spreading,deduced the dynamic evolution equations and elaborated the temporal evolution rules of the processes of information spreading.Based on the real user datasets of sina microblog,the MSLIR algorithm and its dynamical evolution equations were utilized in the computer simulation for the process of microblog information propagation.Compared with several other representative algorithms,the simulation results show the effectiveness and feasibility of the MSLIR algorithm.