  Revealing underlying relations between nodes in a network is one of the most
important tasks in network analysis. Using tools and techniques from a variety
of disciplines, many community recovery methods have been developed for
different scenarios. Despite the recent interest on community recovery in
multilayer networks, theoretical results on the accuracy of the estimates are
few and far between. Given a multilayer, e.g. temporal, network and a
multilayer stochastic block model, we derive bounds for sufficient separation
between intra- and inter-block connectivity parameters to achieve posterior
exact and almost exact community recovery. These conditions are comparable to a
well known threshold for community detection by a single-layer stochastic block
model. A simulation study shows that the derived bounds translate to
classification accuracy that improves as the number of observed layers
increases.
