  A new class of models for dynamic networks is proposed, called mutually
exciting point process graphs (MEG), motivated by a practical application in
computer network security. MEG is a scalable network-wide statistical model for
point processes with dyadic marks, which can be used for anomaly detection when
assessing the significance of previously unobserved connections. The model
combines mutually exciting point processes to estimate dependencies between
events and latent space models to infer relationships between the nodes. The
intensity functions for each network edge are parameterised exclusively by
node-specific parameters, which allows information to be shared across the
network. Fast inferential procedures using modern gradient ascent algorithms
are exploited. The model is tested on simulated graphs and real world computer
network datasets, demonstrating excellent performance.
