  The analysis of multilayer networks is among the most active areas of network
science, and there are now several methods to detect dense "communities" of
nodes in multilayer networks. One way to define a community is as a set of
nodes that trap a diffusion-like dynamical process (usually a random walk) for
a long time. In this view, communities are sets of nodes that create
bottlenecks to the spreading of a dynamical process on a network. We analyze
the local behavior of different random walks on multiplex networks (which are
multilayer networks in which different layers correspond to different types of
edges) and show that they have very different bottlenecks that hence correspond
to rather different notions of what it means for a set of nodes to be a good
community. This has direct implications for the behavior of community-detection
methods that are based on these random walks.
