Inference of targeted interactions of networks with data of driving and driven nodes only by applying fast-varying noise signals

Most complex social, biological and technological systems can be described by dynamic networks. Reconstructing network structures from measurable data is a fundamental problem in almost all interdisciplinary fields. Network nodes interact to each other, therefore, the accurate reconstruction of any interaction to a node requires data measurements of all its neighboring nodes. When networks are large, these data are often unavailable and thus network inference turns to be difficult. Here, we propose a method to use fast-varying noise driving (FVND) to enhance targeted interactions. With applications of noise driving we can infer any interaction from a driving node to a driven node with known data of these two nodes only while all other nodes are hidden, though the driven node may be actually driven by a large number of hidden nodes. Analytical derivation of the FVND method is conducted and numerical simulations perfectly justify the theoretical derivation.