  Dynamic networks offer an insight of how relational systems evolve. However,
modeling these networks efficiently remains a challenge, primarily due to
computational constraints, especially as the number of observed events grows.
This paper addresses this issue by introducing the Deep Relational Event
Additive Model (DREAM) as a solution to the computational challenges presented
by modeling non-linear effects in Relational Event Models (REMs). DREAM relies
on Neural Additive Models to model non-linear effects, allowing each effect to
be captured by an independent neural network. By strategically trading
computational complexity for improved memory management and leveraging the
computational capabilities of Graphic Processor Units (GPUs), DREAM efficiently
captures complex non-linear relationships within data. This approach
demonstrates the capability of DREAM in modeling dynamic networks and scaling
to larger networks. Comparisons with traditional REM approaches showcase DREAM
superior computational efficiency. The model potential is further demonstrated
by an examination of the patent citation network, which contains nearly 8
million nodes and 100 million events.

Machine Learning;Machine Learning;Computation;Methodology