Dynamical and bursty interactions in social networks

We present a modeling framework for dynamical and bursty contact 
networks made of agents in social interaction. We consider 
agents' behavior at short time scales, in which the contact network 
is formed by disconnected cliques of different sizes. At each time 
a random agent can make a transition from being isolated to being 
part of a group, or vice-versa. Different distributions of contact 
times and inter-contact times between individuals are obtained by 
considering transition probabilities with memory effects, i.e. the 
transition probabilities for each agent depend both on its state 
(isolated or interacting) and on the time elapsed since the last 
change of state. The model lends itself to analytical and numerical 
investigations. The modeling framework can be easily extended, and 
paves the way for systematic investigations of dynamical processes 
occurring on rapidly evolving dynamical networks, such as the 
propagation of an information, or spreading of diseases.