A prominent problem in the application of networks to various disciplines is the algorithmic detection of tightly connected groups of nodes known as communities. Recently, there has been increased interest in networks with multiple types of relationships, that change in time, or that network together multiple kinds of networks. We describe different ways that are available for appropriate handling of such "multilayer" features in identifying communities, demonstrating examples with real-world data where such methods have provided new insights.