  Inspired by the increasing interest in self-organizing social opportunistic
networks, we investigate the problem of distributed detection of unknown
communities in dynamic random graphs. As a formal framework, we consider the
dynamic version of the well-studied \emph{Planted Bisection Model}
$\sdG(n,p,q)$ where the node set $[n]$ of the network is partitioned into two
unknown communities and, at every time step, each possible edge $(u,v)$ is
active with probability $p$ if both nodes belong to the same community, while
it is active with probability $q$ (with $q<<p$) otherwise. We also consider a
time-Markovian generalization of this model.
  We propose a distributed protocol based on the popular \emph{Label
Propagation Algorithm} and prove that, when the ratio $p/q$ is larger than
$n^{b}$ (for an arbitrarily small constant $b>0$), the protocol finds the right
"planted" partition in $O(\log n)$ time even when the snapshots of the dynamic
graph are sparse and disconnected (i.e. in the case $p=\Theta(1/n)$).
