Research On Knowledge Dissemination Models With Time-delay And Coherence Mechanisms On Social Networks

In recent years, with the rapid development of information technology, more and more users make communication and exchange with the others through social networks, which have become important channels for people to make communication and learning. In addition, with the development of Educating Information and "Internet +", informal learning has attracted more and more attention from educational experts based on the social networks. Even though spreading knowledge through social network provides great convenience and feasibility for users, but the number of the users in social network is huge, and current research efforts are mainly on the controllability of information propagation in social networks. Not enough attention is paid to the knowledge communication research with time lag effect and coherence mechanism. Therefore, the thesis studies knowledge propagation mechanism and evolution process based on social networks, explores the propagation rule of knowledge in the social networks. We hope that the study will produce important practical impact on China's online education by providing theoretical guidance and technical support.Knowledge is a kind of abstract resources that people can only identify the existence of knowledge through the carrier. The knowledge units are the smallest units in the process of knowledge dissemination, and the propagation process in the social network occurs via the interaction between people, which is similar to the spread of virus in the crowd. Therefore, the thesis based on the research methods of infectious diseases and combines the special attributes of knowledge and the network structure of social networks to study the process of knowledge dissemination. The research works are as follow:(1) Analysis of the characteristics of knowledge dissemination in social networks that the process of knowledge spreading has certain time-delay and social networks have scale-free property. Combined with the infectious disease dynamics model, and using latent nodes to represent the learners who have contact knowledge but not accepted and the knowledge masters who have contacted a large number nodes, this thesis proposes a SEIJR model based on SIR model which is suitable for the dissemination of knowledge in social networks.(2) The application of mathematical tools to deduce the evolution equations for the dynamic model on social network knowledge.(3) Using MATLAB to simulate the deduced dynamic equations and introducing latent-immunes to characterize the users who have touched the knowledge but not completely accepted, giving up the understanding process for the effect of immune persons. These learners has not form a knowledge system, which has negative impact on their subsequence learning process. In the process of simulation, several different parameters are set, and the influence of knowledge spreading rate and transmission rate and super communication mechanism on the immunes and latent immunes are analyzed. Finally, the feasibility and effectiveness of the SEIJR model is verified by comparing the model with SEIR and SIJR model.