Complex human contact networks: Empirical data, modeling and dynamics

In recent years, the possibility to access large digital databases, as
well as the development and deployment of large scale monitoring
frameworks, has allowed to peer for the first time into the statistical
properties of human behavior. Surprisingly, the patterns of human
activity have been shown to be extremely \emph{bursty}, characterized by
long tailed distributions, in opposition to the Poissonian behavior
expected from traditional mathematical approaches. Apart from the
insights that these discoveries have in the description and hypothetical
predictability of human behavior, they are most relevant due to the
direct connection between the patterns of human activity and the
topological description of the representative social networks. Here we
will discuss recent modeling efforts designed to understand and
reproduce the empirical properties of social networks, as well as their
effects on simple dynamical processes.