Network science is an interdisciplinary endeavor with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly connected groups of nodes known as communities. While community detection has been used successfully in a number of applications, its use has been largely limited to the study of single, static networks. Through the study of dynamical processes on networks, we developed a generalized framework of quality functions for partitions that allows for the study of community structure in multislice networks, which are combinations of networks coupled by identification of each node in one network slice to itself in other slices. This framework allows studies of community structure in a general setting encompassing networks that change over time, have multiple types of links, and have multiple community scales.

This work is joint with Thomas Richardson, Kevin Macon, Mason Porter, and Jukka-Pekka Onnela. No prior knowledge about community detection in networks will be assumed for this presentation.