  Dynamic graphs with ordered sequences of events between nodes are prevalent
in real-world industrial applications such as e-commerce and social platforms.
However, representation learning for dynamic graphs has posed great
computational challenges due to the time and structure dependency and irregular
nature of the data, preventing such models from being deployed to real-world
applications. To tackle this challenge, we propose an efficient algorithm,
Efficient Dynamic Graph lEarning (EDGE), which selectively expresses certain
temporal dependency via training loss to improve the parallelism in
computations. We show that EDGE can scale to dynamic graphs with millions of
nodes and hundreds of millions of temporal events and achieve new
state-of-the-art (SOTA) performance.
