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Community detection describes the organization of a network in terms of patterns of connection, identifying tightly connected structures known as communities. A wide variety of methods for community detection have been proposed, with a number of software packages available for performing community detection. In the past decade, there has been increased interest in networks with multiple types of relationships, that change in time, or that network together multiple kinds of networks. With this emphasis on "multilayer networks", new methods and generalizations of existing methods have been developed for performing community detection in these settings. We discuss a variety of recent developments for community detection in multilayer networks, focusing particular attention on the CHAMP algorithm for post-processing partitions to identify the convex hull of admissible modularity partitions and explore the parameter space of multilayer modularity.

This is part of the GGAM Annual Meeting.