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 …

Previous efforts on data-based reconstruction focused on complex networks with pairwise or
two-body interactions. There is a growing interest in networks with higher-order or many …

Based on the binary time series data of social infection dynamics, we propose a general
framework to reconstruct the 2-simplicial complexes with two-body and three-body …

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

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 …

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 …

Social networks have become increasingly popular and are used for various activities. It is
essential to evaluate the trustworthiness of the path between two unknown users in social …

Inferring the structures and the dynamics of the complex networked systems based on time
series data is a challenging problem. The existing reconstruction methods often rely on the …

The community structure detection in static networks often ignores the dynamic nature of the
network and it is difficult to identify the evolution of community structure in dynamic networks …

Link prediction refers to predicting the likelihood of the existence of an unknown link or a
future link based on the observed information. It plays an important role in complex network …