Network Models for Data Science Theory, Algorithms, and Applications

In this chapter, we recognize that the configurations of almost all networks vary with time. We define dynamic networks, which can be observed in discrete or continuous time. Discrete-time dynamic networks can be visualized as a sequence of snapshots of the network taken at different points in time. Continuous-time dynamic networks are more complicated, both visually and theoretically, and assume that edges can appear and disappear continuously through time. We discuss the idea of dynamic community discovery in which community detection strategies are applied to dynamic networks.