一种基于邻居节点间相互影响和改进概率的社交网络信息传播模型

目前网络传播动力学的研究焦点之一是以经典的传染病动力学模型为基础，来研究特定网络的信息传播规律。本文针对社交网络中信息传播的特点，在传统的SIR模型基础上，加入新的一类假免疫节点，建立了SDIR模型。并考虑到邻居节点间的相互影响，定义了三个传播概率函数，对SDIR模型做了改进。通过对比不同条件下信息传播的过程，实验证明了信息不能覆盖全网络，Twitter比新浪微博有更好的信息传播效率的推测，并发现初始传播概率会对信息传播有重要影响。

An Information Propagation Model with Improved Probability Based on Influence of Neighbors for Social Network

One of current focus of spreading dynamics research is analyzing the propagation of information in the specific network based on the epidemic dynamics model. According to the characteristics of propagation in social network, this paper added a kind of new node named disguising node based on the Susceptible-Infectious-Recovered (SIR) model, and proposed a model named Susceptible-Disguising-Infectious- Recovered (SDIR) to describe the propagation better in social network. Considering the mutual influence of neighbor nodes, three propagation probability functions were defined to improve the SDIR model. The results show, by simulating propagation under different conditions, that information cant not cover the whole network, that twitter perform better than Sina Micro-blog in efficiency of propagating. And the initial infection probability have a significant influence in the information propagation.