We present a scalable nonparametric Bayesian method to perform network reconstruction
from observed functional behavior that at the same time infers the communities present in …

The challenging problem of network reconstruction from dynamical data can in general be
formulated as an optimization task of solving multiple linear equations. Existing approaches …

In applications of nonlinear and complex dynamical systems, a common situation is that the
system can be measured, but its structure and the detailed rules of dynamical evolution are …

Accessing the network through which a propagation dynamics diffuses is essential for
understanding and controlling it. In a few cases, such information is available through direct …

Complex networks hosting binary-state dynamics can represent many phenomena in real
world systems. Therefore, some approaches were proposed to reconstruct the structures of …

It has been recognized that many complex dynamical systems in the real world require a
description in terms of multiplex networks, where a set of common, mutually connected …

Methodological advance in reconstructing the structure of a complex dynamical network
have enabled increasingly intricate comprehension of its evolutionary mechanisms and …

Revealing the structure and dynamics of complex networked systems from observed data is
a problem of current interest. Is it possible to develop a completely data-driven framework to …

Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of
previous works, to fully reconstruct the network structure from observed binary data remains …

This study proposed a novel methodology that integrates complex network theory and
multiple time series to enhance the systematic understanding of the daily settlement …