A complex network consists of the underlying topology, defined by a graph and the dynamical processes taking place on a network, defined by a set of governing equations. In this thesis, we deploy the discrete-time linear state-space (DLSS) model to identify the dynamical processes taking place on a complex network. Unlike the black-box identification approach, we split the network into dynamical units and identify the dynamics of each dynamical unit independently. Next, we relate input/output vectors of individual dynamical units, based on the underlying topology and provide the model for the dynamics of the entire network. Because we use a linear model, by scaling the model to the entire network, no information is lost about dynamical processes of individual units.
In this thesis, we apply this new networked system identification solution to two real-world complex networks, water and road networks, and find this identification approach to successfully improve the identification performance compared to the common black-box identification approach.

This thesis’ research concerns the time-dynamics of a complex geographical network of municipalities, i.e. the Dutch Municipality Network over the period 1830-2019. By analysing 190 years of socioeconomic statistical data and applying contemporary tooling from network science and geographic information systems (GIS), the findings from this research can provide a new approach and supportive methods for policymakers, statistical offices, researchers and businesses (to decide when and where to invest).