Study Virus Propagation Model And Analysis Of Complex Networks

Vigorous development of computer technology has made computers play an integral role of people’s lives, but at the same time propagation of the virus on a computer network also cost a great loss to the people. So the research of the mechanism of computer virus propagation and pointing out the key factors in the propagation of the virus has a significance influence on the prevention of computer virus and can also promote the development of relevant policies. In addition, a large number of real-life biological and social systems research can be described with the theory of complex networks. A new approach for people to understand the propagation characteristics of the virus on the network is the complex networks theory. On-line Social Network Service has some differences to traditional complex networks. The user behavioral factors play a non-negligible role in the process of the virus transmission on the social networks. Statistics and some research result show that the simple theory of complex networks is difficult to describe the behavior of the virus spreading on social networks. In the basis of the research on the virus epidemic spreading on complex networks, and inspired by the existing research work, the dissertation focuses on the combination with the user behavior theory, and establish some models to describe the epidemic of the virus propagation on the social networks, which can be in good accordance with the real observations of the real social networks. In addition, we point out some key factors in the spread of the virus propagation on social networks. This dissertation’s main research work is as follows:1. The dissertation studies the influence of the user re-login frequency, the average number of friends on the user’s friend list and the initial spread rate of the virus on the social networks during the epidemic of the propagation of the computer virus. By the means of mathematical analysis, the dissertation establishes a SEIR model to describe the epidemic of the virus on the online social network.2. With the combination of the theory of public opinion transmission, our dissertation introduces the concept of social reinforcement into the propagation of the computer virus. Social reinforcement could explain the phenomenon that on the social network, the probability of being infected would increase if uses receive more signals from their friends. Our dissertation presents a SEIR virus propagation model. In the experiment, we compare the differences between the virus propagation both in regular network and random network By analyzing the experimental results, we come to the conclusion that the two models proposed above can simulate different aspects of the virus propagation on the on-line social network effectively. In model one, the research shows that the re-login frequency of the user and the average friend number of the user would do a significant influence on the virus propagation. The two factors enhance the risk of a virus outbreak in the social network, at the same time. In model two, we compared the virus propagation both in regular network and random network, which comes to the result that:the density of final infected nodes is strongly affected by the combined effects of the primary spreading rate and the social reinforcement, which are two factors in the spreading process. By statistical analysis, we also get the threshold of the social reinforcement. Finally, the analysis of experimental results verifies the effectiveness of the proposed model which can simulate the spread of the virus in a social network effectively.