Large-scale networks with complex interaction patterns are common in nature and society (genetic pathways, ecological networks, social networks, networks of scientific collaboration, WWW, peer-to-peer networks, power grids, etc.). These constantly evolving, networked interactions are critical in determining how dynamical phenomena (like spreading, synchronization, and consensus) behave in complex systems. The aim of this course is to explore the statistical features of both dynamical processes over networks and the temporal evolution of the networks themselves. ‘Dynamics on networks’ refers to processes that take place on top of networks, like the spreading of diseases on human contact networks, the diffusion of ideas and innovations in online social networks, and opinion formation in politics. ‘Dynamics of networks’ refers to mechanisms that provoke changes in the topology of the network, like self-organization in biological systems, cumulative advantage in social hierarchies, and competition in culture. We will introduce the most common mathematical and computational techniques used to characterize dynamical systems on and of networks, including phenomena where both process and network coevolve in time.