Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks,...Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control.Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure.However, these algorithms do not consider network state changes.We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity.By using mean field theory and information entropy to calculate node activity,we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance.We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C.elegans neural network.We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.展开更多
We explore the robustness of a network against failures of vertices or edges where a fraction of vertices is removed and an overload model based on betweenness is constructed.It is assumed that the load and capacity o...We explore the robustness of a network against failures of vertices or edges where a fraction of vertices is removed and an overload model based on betweenness is constructed.It is assumed that the load and capacity of vertex are correlated with its betweenness centrality B_(i)as B_(i)^(θ)and(1+α)B_(i)^(θ)(θis the strength parameter,αis the tolerance parameter).We model the cascading failures following a local load preferential sharing rule.It is found that there exists a minimal whenθis between 0 and 1,and its theoretical analysis is given.The minimalα_(c)characterizes the strongest robustness of a network against cascading failures triggered by removing a random fraction f of vertices.It is realized that the minimalα_(c)increases with the increase of the removal fraction f or the decrease of average degree.In addition,we compare the robustness of networks whose overload models are characterized by degree and betweenness,and find that the networks based on betweenness have stronger robustness against the random removal of a fraction f of vertices.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant Nos.72071153 and 72231008)Laboratory of Science and Technology on Integrated Logistics Support Foundation (Grant No.6142003190102)the Natural Science Foundation of Shannxi Province (Grant No.2020JM486)。
文摘Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control.Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure.However, these algorithms do not consider network state changes.We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity.By using mean field theory and information entropy to calculate node activity,we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance.We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C.elegans neural network.We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.
基金the National Natural Science Foundation of China(Grant Nos.71771186,71631001,and 72071153)the Natural Science Foundation of Shaanxi Province,China(Grant Nos.2020JM-486 and 2020JM-486).
文摘We explore the robustness of a network against failures of vertices or edges where a fraction of vertices is removed and an overload model based on betweenness is constructed.It is assumed that the load and capacity of vertex are correlated with its betweenness centrality B_(i)as B_(i)^(θ)and(1+α)B_(i)^(θ)(θis the strength parameter,αis the tolerance parameter).We model the cascading failures following a local load preferential sharing rule.It is found that there exists a minimal whenθis between 0 and 1,and its theoretical analysis is given.The minimalα_(c)characterizes the strongest robustness of a network against cascading failures triggered by removing a random fraction f of vertices.It is realized that the minimalα_(c)increases with the increase of the removal fraction f or the decrease of average degree.In addition,we compare the robustness of networks whose overload models are characterized by degree and betweenness,and find that the networks based on betweenness have stronger robustness against the random removal of a fraction f of vertices.
