Finding the important nodes in complex networks by topological structure is of great significance to network invulnerability.Several centrality measures have been proposed recently to evaluate the performance of nodes...Finding the important nodes in complex networks by topological structure is of great significance to network invulnerability.Several centrality measures have been proposed recently to evaluate the performance of nodes based on their correlation,showing that the interaction between nodes has an influence on the importance of nodes.In this paper,a novel method based on node’s distribution and global influence in complex networks is proposed.The nodes in the complex networks are classified according to the distance matrix,then the correlation coefficient between pairs of nodes is calculated.From the whole perspective in the network,the global similarity centrality(GSC)is proposed based on the relevance and the shortest distance between any two nodes.The efficiency,accuracy,and monotonicity of the proposed method are analyzed in two artificial datasets and eight real datasets of different sizes.Experimental results show that the performance of GSC method outperforms those current state-of-the-art algorithms.展开更多
A stochastic susceptible-infective-recovered(SIR)epidemic model with jumps was considered.The contributions of this paper are as follows.(1) The stochastic differential equation(SDE)associated with the model has a uni...A stochastic susceptible-infective-recovered(SIR)epidemic model with jumps was considered.The contributions of this paper are as follows.(1) The stochastic differential equation(SDE)associated with the model has a unique global positive solution;(2) the results reveal that the solution of this epidemic model will be stochastically ultimately bounded,and the non-linear SDE admits a unique stationary distribution under certain parametric conditions;(3) the coefficients play an important role in the extinction of the diseases.展开更多
基金the National Natural Science Foundation of China(Nos.11361033,62162040 and 11861045)。
文摘Finding the important nodes in complex networks by topological structure is of great significance to network invulnerability.Several centrality measures have been proposed recently to evaluate the performance of nodes based on their correlation,showing that the interaction between nodes has an influence on the importance of nodes.In this paper,a novel method based on node’s distribution and global influence in complex networks is proposed.The nodes in the complex networks are classified according to the distance matrix,then the correlation coefficient between pairs of nodes is calculated.From the whole perspective in the network,the global similarity centrality(GSC)is proposed based on the relevance and the shortest distance between any two nodes.The efficiency,accuracy,and monotonicity of the proposed method are analyzed in two artificial datasets and eight real datasets of different sizes.Experimental results show that the performance of GSC method outperforms those current state-of-the-art algorithms.
基金Natural Science Foundation of Hunan University of Technology,China(No.2012HZX08)the Special Foundation of National Independent Innovation Demonstration Area Construction of Zhuzhou(Applied Basic Research),China
文摘A stochastic susceptible-infective-recovered(SIR)epidemic model with jumps was considered.The contributions of this paper are as follows.(1) The stochastic differential equation(SDE)associated with the model has a unique global positive solution;(2) the results reveal that the solution of this epidemic model will be stochastically ultimately bounded,and the non-linear SDE admits a unique stationary distribution under certain parametric conditions;(3) the coefficients play an important role in the extinction of the diseases.
