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
Ecosystems generally have the self-adapting ability to resist various external pressures or disturbances,which is always called resilience.However,once the external disturbances exceed the tipping points of the system...Ecosystems generally have the self-adapting ability to resist various external pressures or disturbances,which is always called resilience.However,once the external disturbances exceed the tipping points of the system resilience,the consequences would be catastrophic,and eventually lead the ecosystem to complete collapse.We capture the collapse process of ecosystems represented by plant-pollinator networks with the k-core nested structural method,and find that a sufficiently weak interaction strength or a sufficiently large competition weight can cause the structure of the ecosystem to collapse from its smallest k-core towards its largest k-core.Then we give the tipping points of structure and dynamic collapse of the entire system from the one-dimensional dynamic function of the ecosystem.Our work provides an intuitive and precise description of the dynamic process of ecosystem collapse under multiple interactions,and provides theoretical insights into further avoiding the occurrence of ecosystem collapse.展开更多
To identify the unstable individuals of networks is of great importance for information mining and security management.Exploring a broad range of steady-state dynamical processes including biochemical dynamics,epidemi...To identify the unstable individuals of networks is of great importance for information mining and security management.Exploring a broad range of steady-state dynamical processes including biochemical dynamics,epidemic processes,birth-death processes and regulatory dynamics,we propose a new index from the microscopic perspective to measure the stability of network nodes based on the local correlation matrix.The proposed index describes the stability of each node based on the activity change of the node after its neighbor is disturbed.Simulation and comparison results show our index can identify the most unstable nodes in the network with various dynamical behaviors,which would actually create a richer way and a novel insight of exploring the problem of network controlling and optimization.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Project supported by the National Natural Science Foundation of China(Grant Nos.72071153 and 72231008)the Natural Science Foundation of Shaanxi Province(Grant No.2020JM-486)the Fund of the Key Laboratory of Equipment Integrated Support Technology(Grant No.6142003190102)。
文摘Ecosystems generally have the self-adapting ability to resist various external pressures or disturbances,which is always called resilience.However,once the external disturbances exceed the tipping points of the system resilience,the consequences would be catastrophic,and eventually lead the ecosystem to complete collapse.We capture the collapse process of ecosystems represented by plant-pollinator networks with the k-core nested structural method,and find that a sufficiently weak interaction strength or a sufficiently large competition weight can cause the structure of the ecosystem to collapse from its smallest k-core towards its largest k-core.Then we give the tipping points of structure and dynamic collapse of the entire system from the one-dimensional dynamic function of the ecosystem.Our work provides an intuitive and precise description of the dynamic process of ecosystem collapse under multiple interactions,and provides theoretical insights into further avoiding the occurrence of ecosystem collapse.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72071153 and 71771186)Key Laboratory of Science and Technology on Integrated Logistics Support(Grant Nos.6142003190102)+1 种基金the Natural Science Foundation of Shaanxi Province,China(Grant Nos.2020JM-486)the China Postdoctoral Science Foundation(Grant No.2017M613336).
文摘To identify the unstable individuals of networks is of great importance for information mining and security management.Exploring a broad range of steady-state dynamical processes including biochemical dynamics,epidemic processes,birth-death processes and regulatory dynamics,we propose a new index from the microscopic perspective to measure the stability of network nodes based on the local correlation matrix.The proposed index describes the stability of each node based on the activity change of the node after its neighbor is disturbed.Simulation and comparison results show our index can identify the most unstable nodes in the network with various dynamical behaviors,which would actually create a richer way and a novel insight of exploring the problem of network controlling and optimization.
