Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks Alexander Modell 1 Ian Gallagher 2 Emma Ceccherini 2 Nick Whiteley

We present a new representation learning framework, Intensity Profile Projection, for continuous-time dynamic network data. Given triples (i, j, t), each representing a time-stamped (t) interaction between two entities (i, j), our procedure returns a continuous-time trajectory for each node, representing its behaviour over time. The framework consists of three stages: estimating pairwise intensity functions, e.g.