A Kind of Improved Susceptible-Infected Model

By analyzing the susceptible-infected model, susceptible-infected-recovered-susceptible model and susceptible infected-recovered model, we get the improved Kermachk-Mckendrick model. And by applying the controlled threshold value, we get the conditions of isolated rate for infectious disease eventually disappeared.

Influential nodes identification in complex networks based on global and local information

Identifying influential nodes in complex networks is essential for network robust and stability,such as viral marketing and information control.Various methods have been proposed to define the influence of nodes.In this paper,we comprehensively consider the global position and local structure to identify influential nodes.The number of iterations in the process of k-shell decomposition is taken into consideration,and the improved k-shell decomposition is then put forward.The improved k-shell decomposition and degree of target node are taken as the benchmark centrality,in addition,as is well known,the effect between node pairs is inversely proportional to the shortest path length between two nodes,and then we also consider the effect of neighbors on target node.To evaluate the performance of the proposed method,susceptible-infected(SI)model is adopted to simulate the spreading process in four real networks,and the experimental results show that the proposed method has obvious advantages over classical centrality measures in identifying influential nodes.

Virus spreading in wireless sensor networks with a medium access control mechanism

In this paper, an extended version of standard susceptible-infected （SI） model is proposed to consider the influence of a medium access control mechanism on virus spreading in wireless sensor networks. Theoretical analysis shows that the medium access control mechanism obviously reduces the density of infected nodes in the networks, which has been ignored in previous studies. It is also found that by increasing the network node density or node communication radius greatly increases the number of infected nodes. The theoretical results are confirmed by numerical simulations.

An application of small-world network on predicting the behavior of infectious disease on campus

Networks haven been widely used to understand the spread of infectious disease.This study examines the properties of small-world networks in modeling infectious disease on campus.Two different small-world models are developed and the behaviors of infectious disease in the models are observed through numerical simulations.The results show that the behavior pattern of infectious disease in a small-world network is different from those in a regular network or a random network.The spread of the infectious disease increases as the proportion of long-distance connections p increasing,which indicates that reducing the contact among people is an effective measure to control the spread of infectious disease.The probability of node position exchange in a network(p2)had no significant effect on the spreading speed,which suggests that reducing human mobility in closed environments does not help control infectious disease.However,the spreading speed is proportional to the number of shared nodes(s),which means reducing connections between different groups and dividing students into separate sections will help to control infectious disease.In the end,the simulating speed of the small-world network is tested and the quadratic relationship between simulation time and the number of nodes may limit the application of the SW network in areas with large populations.