With the development of online social networks, they rapidly become an ideal platform for information about social information diffusion, commodity marketing, shopping recommendation, opinion expression and social consensus. The social network information propagation has become a research hotspot correspondingly. Meanwhile, information diffu-sion contains complex dynamic genesis in online social networks. In view of the diversity of information transmission, the efficiency of propagation and the convenience of interaction, it is very important to regulate the accuracy, strengthen the public opinion monitoring and formulating the information control strategy. The purpose of this study is to quantify the intensity of the influence, especially provides a theoretical basis for studying the state transition of different user groups in the evolution process. As existing epidemic model paid less at-tention to influence factors and previous research about influence calculation mainly focused on static network topology but ignored individual behavior characteristics, we propose an information diffusion dynamics model based on dynamic user behaviors and influence. Firstly, according to the multiple linear regression model, we put forward a method to analyze internal and external factors for influence formation from two aspects: personal memory and user interaction. Secondly, for a similar propagation mechanism of information diffusion and epidemics spreading, in this paper we present an improved SIR model based on mean-field theory by introducing influence factor. The contribution of this paper can be summarized as follows. 1) For the influence quantification, different from the current research work that mainly focuses on network structure, we integrate the internal factors and external factors, and propose a user influence evaluation method based on the multiple linear regression model. The individual memory principle is analyzed by combining user attributes and individual behavior. User interaction is also studied by using the shortest path method in graph theory. 2) On modeling the information diffusion, by referring SIR model, we introduce the user influence factor as the parameter of the state change into the epidemic model. The mean-field theory is used to establish the differential equations. Subsequently, the novel information diffusion dynamics model and verification method are proposed. The method avoids the randomness of the artificial setting parameters within the model, and reveals the nature of multi-factors coupling in the information transmission. Experimental results show that the optimized model can comprehend the principle and information diffusion mech-anism of social influence from a more macroscopic level. The study can not only explain the internal and external dynamics genesis of information diffusion, but also explore the behavioral characteristics and behavior laws of human. In addition, we try to provide theoretical basis for situation awareness and control strategy of social information diffusion.%在线社会网络中,信息传播蕴含着复杂的动力学成因.本文将传染病模型与社交影响力要素相结合,并针对影响力度量中主要研究静态网络拓扑结构、忽略个体行为特征的问题,提出一种基于动态节点行为和用户影响力的信息传播动力学模型,旨在量化影响力强度,为研究信息扩散过程中不同用户群体状态转变提供理论依据.首先,在网络拓扑结构和用户行为两方面,提取个人记忆和用户交互两个表征,分析影响力形成的内因和外因两个动力学成因,并基于多元线性回归模型,提出一种度量用户社会影响力的方法.其次,在传统传染病SIR(susceptible-infected-recovered)模型基础上,结合信息扩散与传染病蔓延相似的传播机理,综合考虑信息传播的多源并发性和双向性,引入影响力因子,利用平均场理论改进得到一种基于用户影响力的信息传播模型.实验表明,该模型能有效地解释在线社会网络中信息传播的动力学原因,感知社会网络中信息传播演化态势.
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