This paper aims to provide a comprehensive critical overview on how entities and their
interactions in Complex Networked Systems (CNS) are modelled across disciplines as they …

This scholarly exploration delves into the dynamic intersection of machine learning (ML) and
tax prediction, a realm where the fusion of advanced computational techniques and fiscal …

The COVID‐19 pandemic has led to the unprecedented challenge of devising massive
vaccination rollouts, toward slowing down and eventually extinguishing the diffusion of the …

In this paper, we propose a Boltzmann-type kinetic model of the spreading of an infectious
disease on a network. The latter describes the connections among countries, cities or …

The spread of an epidemic disease and the population's collective behavioral response are
deeply intertwined, influencing each other's evolution. Such a co-evolution typically has …

Advanced persistent threat (APT) is a kind of stealthy, sophisticated, and long-term
cyberattack that has brought severe financial losses and critical infrastructure damages …

This paper studies a discrete-time time-varying multi-layer networked SIWS (susceptible-
infected-water-susceptible) model with multiple resources under both single-virus and …

A key scientific challenge during the outbreak of novel infectious diseases is to predict how
the course of the epidemic changes under countermeasures that limit interaction in the …

Traditional network analysis focuses on the representation of complex systems with only
pairwise interactions between nodes. However, the higher-order structure, which is beyond …

The ongoing COVID-19 pandemic has led public health authorities to face the
unprecedented challenge of planning a global vaccination campaign, which for most …