Epidemiological contact network models have emerged as an important tool in
understanding and predicting spread of infectious disease, due to their capacity to engage …

Many dynamical systems can be successfully analyzed by representing them as networks.
Empirically measured networks and dynamic processes that take place in these situations …

Current understanding of the critical outbreak condition on temporal networks relies on
approximations (time scale separation, discretization) that may bias the results. We propose …

Identifying the hidden organizational principles and relevant structures of complex networks
is fundamental to understand their properties. To this end, uncovering the structures …

We present a contact-based model to study the spreading of epidemics by means of
extending the dynamic message-passing approach to temporal networks. The shift in …

Temporal networks are widely used to represent a vast diversity of systems, including in
particular social interactions, and the spreading processes unfolding on top of them. The …

Locating influential nodes in temporal networks has attracted a lot of attention as data driven
and diverse applications. Classic works either looked at analysing static networks or placed …

Changes in individual behavior often entangle with the dynamic interaction of individuals,
which complicates the epidemic process and brings great challenges for the understanding …

Multilayer networks allow one to represent diverse and coupled connectivity patterns—such
as time-dependence, multiple subsystems, or both—that arise in many applications and …

Some diseases can live in people for many years without making them sick, during this time,
the bacteria can spread to others who come in contact with the infected person. However …