Distributed Community Detection in Dynamic Graphs

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