  The identification of community structure in a social network is an important
problem tackled in the literature of network analysis. There are many solutions
to this problem using a static scenario, when facing a dynamic scenario some
solutions may be adapted but others simply do not fit, moreover when
considering the demand to analyze constantly growing networks. In this context,
we propose an approach to the problem of community detection in dynamic
networks based on a reinforcement learning strategy to deal with changes on big
networks using a local optimization on the modularity score of the changed
entities. An experiment using synthetic and real-world dynamic network data
shows results comparable to static scenarios.
