The problem of reconstructing nonlinear and complex dynamical systems from measured
data or time series is central to many scientific disciplines including physical, biological …

Complex systems are extremely hard to predict due to its highly nonlinear interactions and
rich emergent properties. Thanks to the rapid development of network science, our …

We study the relationship between dynamical properties and interaction patterns in complex
oscillator networks in the presence of noise. A striking finding is that noise leads to a …

Evolutionary games model a common type of interactions in a variety of complex, networked,
natural systems and social systems. Given such a system, uncovering the interacting …

Complex dynamical networks consisting of a large number of interacting units are ubiquitous
in nature and society. There are situations where the interactions in a network of interest are …

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 …

Nowadays, massive amounts of data are available for analysis in natural and social systems
and the tasks to depict system structures from the data, ie, the inverse problems, become …

In a complex system, the interactions between individual agents often lead to emergent
collective behavior such as spontaneous synchronization, swarming, and pattern formation …

Ascertaining the existence of hidden objects in a complex system, objects that cannot be
observed from the external world, not only is curiosity-driven but also has significant …

In the study of networked systems, a method that can extract information about how the
individual nodes are connected with one another would be valuable. In this paper, we …