In the last decade, there has been a growing body of literature addressing the utilization of
complex network methods for the characterization of dynamical systems based on time …

The ongoing rapid expansion of the Word Wide Web (WWW) greatly increases the
information of effective transmission from heterogeneous individuals to various systems …

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

Reconstructing complex networks from measurable data is a fundamental problem for
understanding and controlling collective dynamics of complex networked systems. However …

Our ability to uncover complex network structure and dynamics from data is fundamental to
understanding and controlling collective dynamics in complex systems. Despite recent …

Stock time series forecasting is a universal purpose of academic researchers, even a slight
improvement in the accuracy of the forecast may have a fabulous impact on participants' …

Existing methods for gene regulatory network (GRN) inference rely on gene expression data
alone or on lower resolution bulk data. Despite the recent integration of chromatin …

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 …

Quantifying causality between variables from observed time series data is of great
importance in various disciplines but also a challenging task, especially when the observed …

The multiscale phenomenon widely exists in nonlinear complex systems. One efficient way
to characterize complex systems is to measure time series and then extract information from …