基于行为分析与传播个体的微博传播模型研究

随着社交网络的迅速发展,掌握信息传播的规律,进行信息传播的管理越来越重要,而提取信息传播特点和建立模型已经成为研究热点。传统信息传播模型并未考虑易感染者的类型以及时间网络等行为因素,对信息传播规律的研究不够准确。针对信息传播及用户行为的分析,改进模型增加双重易感染者、潜伏者等传播个体和优化传播过程的微博网络传播算法(MDSLIR)。该算法使得用户可以根据传播方式及时获取、传播和免疫信息,从而更好对社交网络信息进行管理。以新浪微博为例,研究微博信息的演化规律,使用微博上的真实数据进行仿真测试,并与传统算法进行比较,实验结果表明MDSLIR算法的有效性和可行性。

Research on Microblog Propagation Model Based on Behavior Analysis and Propagation

With the rapid development of social networks, it is more and more important to master the law of information dissemination and the management of information dissemination. The extraction of information dissemination characteristics and model building has become a research hotspot. The traditional information dissemination model does not consider the types of vulnerable people and the behavioral factors such as time network, and the research on the law of information dissemination is not accurate enough. For the analysis of information dissemination and user behavior, the improved model increases the micro-blog network propagation algorithm (MDSLIR), which is a dual-susceptible person, a latent person, etc., and an optimized propagation process. The algorithm enables users to acquire, disseminate and immunize information in a timely manner according to the propagation mode, thereby better managing social network information. Taking Sina Weibo as an example, the evolution law of microblog information is studied. The real data on Weibo is used for simulation test and compared with traditional algorithms. The experimental results show the validity and feasibility of MDSLIR algorithm.