Inferring network structures from available data has attracted much interest in network science;however,in many realistic networks,only some of the nodes are perceptible while others are hidden,making it a challenging...Inferring network structures from available data has attracted much interest in network science;however,in many realistic networks,only some of the nodes are perceptible while others are hidden,making it a challenging task.In this work,we develop a method for reconstructing the network with hidden nodes and links,taking account of fast-varying noise and time-delay interactions.By calculating the correlations of available data with different derivative orders for multiple pairs of accessible nodes,analyzing and integrating the relationships between different correlations,and defining diverse hidden-node-related reconstruction motifs,we can effectively identify the hidden nodes and hidden links in the network.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.11835003)supported by the National Natural Science Foundation of China(Grant Nos.12375033,12235007,and 11975131)+7 种基金the Natural Science Foundation of Zhejiang(Grant No.LY23A050002)the K.C.Wong Magna Fund at Ningbo Universitysupported by the National Natural Science Foundation of China(Grant No.T2122016)the National Science and Technology Innovation 2030 Major Program(Grant Nos.2021ZD0203700,and 2021ZD0203705)the Fundamental Research Funds for the Central Universities(Grant No.2022CDJKYJH034)supported by the National Institutes of Health(Grant Nos.R01 HL134709,R01 HL139829,R01 HL157116,and P01 HL164311)supported by the National Natural Science Foundation of China(Grant No.11905291)CAS Project for Young Scientists in Basic Research(Grant No.YSBR-041)。
文摘Inferring network structures from available data has attracted much interest in network science;however,in many realistic networks,only some of the nodes are perceptible while others are hidden,making it a challenging task.In this work,we develop a method for reconstructing the network with hidden nodes and links,taking account of fast-varying noise and time-delay interactions.By calculating the correlations of available data with different derivative orders for multiple pairs of accessible nodes,analyzing and integrating the relationships between different correlations,and defining diverse hidden-node-related reconstruction motifs,we can effectively identify the hidden nodes and hidden links in the network.
