  Multiplex networks are a type of multilayer network in which entities are
connected to each other via multiple types of connections. We propose a method,
based on computing pairwise similarities between layers and then doing
community detection, for grouping structurally similar layers in multiplex
networks. We illustrate our approach using both synthetic and empirical
networks, and we are able to find meaningful groups of layers in both cases.
For example, we find that airlines that are based in similar geographic
locations tend to be grouped together in an airline multiplex network and that
related research areas in physics tend to be grouped together in an multiplex
collaboration network.
