Models and methodology for network time series are a rich area of research due to their wide applicability. However, far less developed but of equal worth is when networks are characterised by point processes on nodes. Examples are rife; disease outbreaks are represented by infection, hospitalisation, recovery and death events; nodes on computer networks are performing a set of tasks indexed in time; public transport networks record passenger events; delivery companies record arrival and dispatch events. These point processes often reveal themselves as a series of counts due to data recording or storage limitations. Formulating bespoke models for network data of this type is a challenging and pressing issue with potential for significant impact in a range of applications. Working with our partners, NETC will deliver scalable, interpretable and identifiable network models for count processes that can leverage known network structure, external variables and node features, to forecast future event counts and detect anomalous behaviour.