  A multiplex network models different modes of interaction among same-type
entities. In this article we provide a taxonomy of community detection
algorithms in multiplex networks. We characterize the different algorithms
based on various properties and we discuss the type of communities detected by
each method. We then provide an extensive experimental evaluation of the
reviewed methods to answer three main questions: to what extent the evaluated
methods are able to detect ground-truth communities, to what extent different
methods produce similar community structures and to what extent the evaluated
methods are scalable. One goal of this survey is to help scholars and
practitioners to choose the right methods for the data and the task at hand,
while also emphasizing when such choice is problematic.
