Information Search Algorithm Based on Markov Model in the Mobile Social Network

In the era of big data, how to find the source of information diffusion from massive social media and predict the future trend of information diffusion has become a hot issue that people are very concerned about, which presents new challenges for the application of data mining in hot issues. The most influential information mining methods can be divided into static and dynamic. The static method is only specific to a point in time, and has its one-sided views. There are four kinds of dynamic methods commonly used, all of which have their limitations. The independent cascading model and the linear threshold model are the most widely used, both of which require the activation process to be placed in a discrete cycle and are discontinuous. This paper independently studies a model based on continuous time Markov process to simulate the information diffusion in the real world, and predicts the information diffusion ability of each node in the future according to the information diffusion situation, so as to achieve the purpose of judging the future trend.