This paper is concerned with a stochastic HBV infection model with logistic growth. First, by constructing suitable stochastic Lyapunov functions, we establish sufficient conditions for the existence of ergodic statio...This paper is concerned with a stochastic HBV infection model with logistic growth. First, by constructing suitable stochastic Lyapunov functions, we establish sufficient conditions for the existence of ergodic stationary distribution of the solution to the HBV infection model. Then we obtain sufficient conditions for extinction of the disease. The stationary distribution shows that the disease can become persistent in vivo.展开更多
Identifying influential nodes in complex networks is one of the most significant and challenging issues,which may contribute to optimizing the network structure,controlling the process of epidemic spreading and accele...Identifying influential nodes in complex networks is one of the most significant and challenging issues,which may contribute to optimizing the network structure,controlling the process of epidemic spreading and accelerating information diffusion.The node importance ranking measures based on global information are not suitable for large-scale networks due to their high computational complexity.Moreover,they do not take into account the impact of network topology evolution over time,resulting in limitations in some applications.Based on local information of networks,a local clustering H-index(LCH)centrality measure is proposed,which considers neighborhood topology,the quantity and quality of neighbor nodes simultaneously.The proposed measure only needs the information of first-order and second-order neighbor nodes of networks,thus it has nearly linear time complexity and can be applicable to large-scale networks.In order to test the proposed measure,we adopt the susceptible-infected-recovered(SIR)and susceptible-infected(SI)models to simulate the spreading process.A series of experimental results on eight real-world networks illustrate that the proposed LCH can identify and rank influential nodes more accurately than several classical and state-of-the-art measures.展开更多
基金supported by NSFC of China(11371085)the Fundamental Research Funds for the Central Universities(15CX08011A),2016GXNSFBA380006 and KY2016YB370
文摘This paper is concerned with a stochastic HBV infection model with logistic growth. First, by constructing suitable stochastic Lyapunov functions, we establish sufficient conditions for the existence of ergodic stationary distribution of the solution to the HBV infection model. Then we obtain sufficient conditions for extinction of the disease. The stationary distribution shows that the disease can become persistent in vivo.
基金Project supported by the National Natural Foundation of China(Grant No.11871328)the Shanghai Science and Technology Development Funds Soft Science Research Project(Grant No.21692109800).
文摘Identifying influential nodes in complex networks is one of the most significant and challenging issues,which may contribute to optimizing the network structure,controlling the process of epidemic spreading and accelerating information diffusion.The node importance ranking measures based on global information are not suitable for large-scale networks due to their high computational complexity.Moreover,they do not take into account the impact of network topology evolution over time,resulting in limitations in some applications.Based on local information of networks,a local clustering H-index(LCH)centrality measure is proposed,which considers neighborhood topology,the quantity and quality of neighbor nodes simultaneously.The proposed measure only needs the information of first-order and second-order neighbor nodes of networks,thus it has nearly linear time complexity and can be applicable to large-scale networks.In order to test the proposed measure,we adopt the susceptible-infected-recovered(SIR)and susceptible-infected(SI)models to simulate the spreading process.A series of experimental results on eight real-world networks illustrate that the proposed LCH can identify and rank influential nodes more accurately than several classical and state-of-the-art measures.
