Community Partition immunization strategy based on Search Engine

Abstract

People's dependence on search engines allows various computer viruses to spread faster and stronger. Most scholars have neglected the influence of search engines on virus propagation and immunity. It is impossible to immunize all users at the same time with a huge system like social networks. So the main problem is how to pick a fixed-scale node cluster as the source of immunity in the network, which can make other individuals immune and continue to spread (called immune seeds). The immune seeds are scattered on some web pages of search engines to reduce the network virus infection rate. We establish two models, one is the model of computer virus early propagation based on the search engine, and the other is the model of the virus propagation and immunization model. Then we propose an improved immunization strategy: Community Partition immunization strategy based on the target immunization strategy. And we use four real datasets and two simulated datasets to do the simulation experiments, which shows that search engine can promote the propagation of the virus and the immune seeds, and the efficiency of the Community Partition immunization strategy is slightly higher than the target immunization strategy based on degree under the same conditions.