title: Detecting hidden nodes in complex networks from time series.
abstract: We develop a general method to detect hidden nodes in complex networks, using only time series from nodes that are accessible to external observation. Our method is based on compressive sensing and we formulate a general framework encompassing continuous- and discrete-time and the evolutionary-game type of dynamical systems as well. For concrete demonstration, we present an example of detecting hidden nodes from an experimental social network. Our paradigm for detecting hidden nodes is expected to find applications in a variety of fields where identifying hidden or black-boxed objects based on a limited amount of data is of interest.
authors.affiliation: School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA.
authors.forename: Ri-Qi
authors.initials: RQ
authors.lastname: Su
authors.forename: Wen-Xu
authors.initials: WX
authors.lastname: Wang
authors.forename: Ying-Cheng
authors.initials: YC
authors.lastname: Lai
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D015233:Models, Statistical