Research On Virus Spreading And Immunization Strategy In Weighted Networks

Depth study of biological networks, computer networks and other real networks shows that weighted networks could describe the real networks better than unweighted networks. Their unique weights can accurately describe the familiarity of the relationship between people and traffic between network nodes. Research on virus spreading and immunization strategy is an important field of complex networks.Based on BBV weighted networks’ characteristic, using the classic susceptible-infected model and considering the difference of the viral spreading probability between different nodes, this paper proposes two immunization strategies. And the simulation analysis is carried out on the artificial networks and real networks. This paper focuses on virus spreading model and immunization strategy in weighted networks. Details of the work and results are as follows:First, based on acquaintance immunization in unweighted networks, using weight-priority, this paper proposes the improved acquaintance immunization based on weight-priority, IAI-WP. The simulation results in the artificial networks and real networks show that, this immunization strategy gets lower density of infected individuals and has a better effect than the classic acquaintance immunization. And, it needs lower computational complexity and less nodes’ information than the targeted immunization. Besides, the gap of effect between the two strategies is increasing along with the increasing density of deleted nodes. Moreover, it is shown that the more obvious the preferential attachment in BBV networks is, the better the effect of IAI-WP is.Second, based on weighted networks’ characteristic, using weight-priority, this paper finds a depth search method to get some nodes whose strength is larger in networks according to local information. On the basis of this, the depth search immunization based on weight-priority, DSI-WP,is proposed. The simulation results show that, only with some nodes’ information, this strategy is better than the classic acquaintance immunization. Unlike IAI-WP, the gap of effect between the two strategies is decreasing along with the increasing density of deleted nodes. Also, the more obvious the preferential attachment in BBV networks is, the better the effect of DSI-WP is.Third, comparing the two immunization strategies and analyzing the differences. Research shows, IAI-WP is better than DSI-WP, overall. But when the density of deleted nodes is low,DSI-WP is better. Moreover, the gap of effect between the two strategies is increasing along with the increasing density of deleted nodes. The sum of the immune nodes’ strength is the direct cause of effect between two strategies.The two strategies this paper proposed need lower computational complexity, require less nodes’ information, and they are practical.