With the development of network science,the coupling between networks has become the focus of complex network research.However,previous studies mainly focused on the coupling between nodes,while ignored the coupling b...With the development of network science,the coupling between networks has become the focus of complex network research.However,previous studies mainly focused on the coupling between nodes,while ignored the coupling between edges.We propose a novel cascading failure model of two-layer networks.The model considers the different loads and capacities of edges,as well as the elastic and coupling relationship between edges.In addition,a more flexible load-capacity strategy is adopted to verify the model.The simulation results show that the model is feasible.Different networks have different behaviors for the same parameters.By changing the load parameters,capacity parameters,overload parameters,and distribution parameters reasonably,the robustness of the model can be significantly improved.展开更多
How to identify influential nodes in complex networks is an essential issue in the study of network characteristics.A number of methods have been proposed to address this problem,but most of them focus on only one asp...How to identify influential nodes in complex networks is an essential issue in the study of network characteristics.A number of methods have been proposed to address this problem,but most of them focus on only one aspect.Based on the gravity model,a novel method is proposed for identifying influential nodes in terms of the local topology and the global location.This method comprehensively examines the structural hole characteristics and K-shell centrality of nodes,replaces the shortest distance with a probabilistically motivated effective distance,and fully considers the influence of nodes and their neighbors from the aspect of gravity.On eight real-world networks from different fields,the monotonicity index,susceptible-infected-recovered(SIR)model,and Kendall’s tau coefficient are used as evaluation criteria to evaluate the performance of the proposed method compared with several existing methods.The experimental results show that the proposed method is more efficient and accurate in identifying the influence of nodes and can significantly discriminate the influence of different nodes.展开更多
基金the National Natural Science Foundation of China(Grant No.61663030)the Natural Science Foundation of Jiangxi Province,China(Grant No.20142BAB207021)the Innovation Fund Designated for Graduate Students of Jiangxi Province,China(Grant No.YC2021-S680).
文摘With the development of network science,the coupling between networks has become the focus of complex network research.However,previous studies mainly focused on the coupling between nodes,while ignored the coupling between edges.We propose a novel cascading failure model of two-layer networks.The model considers the different loads and capacities of edges,as well as the elastic and coupling relationship between edges.In addition,a more flexible load-capacity strategy is adopted to verify the model.The simulation results show that the model is feasible.Different networks have different behaviors for the same parameters.By changing the load parameters,capacity parameters,overload parameters,and distribution parameters reasonably,the robustness of the model can be significantly improved.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61663030 and 61663032)。
文摘How to identify influential nodes in complex networks is an essential issue in the study of network characteristics.A number of methods have been proposed to address this problem,but most of them focus on only one aspect.Based on the gravity model,a novel method is proposed for identifying influential nodes in terms of the local topology and the global location.This method comprehensively examines the structural hole characteristics and K-shell centrality of nodes,replaces the shortest distance with a probabilistically motivated effective distance,and fully considers the influence of nodes and their neighbors from the aspect of gravity.On eight real-world networks from different fields,the monotonicity index,susceptible-infected-recovered(SIR)model,and Kendall’s tau coefficient are used as evaluation criteria to evaluate the performance of the proposed method compared with several existing methods.The experimental results show that the proposed method is more efficient and accurate in identifying the influence of nodes and can significantly discriminate the influence of different nodes.
