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.
@article{arxiv.2112.07768,
title = {Efficient Dynamic Graph Representation Learning at Scale},
author = {Xinshi Chen and Yan Zhu and Haowen Xu and Mengyang Liu and Liang Xiong and Muhan Zhang and Le Song},
journal= {arXiv preprint arXiv:2112.07768},
year = {2021}
}