Real-world graphs, such as social networks, financial transactions, and
recommendation systems, often demonstrate dynamic behavior. This phenomenon,
known as graph stream, involves the dynamic changes of nodes and the emergence
and disappearance of edges. To effectively capture both the structural and
temporal aspects of these dynamic graphs, dynamic graph neural networks have
been developed. However, existing methods are usually tailored to process
either continuous-time or discrete-time dynamic graphs, and cannot be
generalized from one to the other. In this paper, we propose a decoupled graph
neural network for large dynamic graphs, including a unified dynamic
propagation that supports efficient computation for both continuous and
discrete dynamic graphs. Since graph structure-related computations are only
performed during the propagation process, the prediction process for the
downstream task can be trained separately without expensive graph computations,
and therefore any sequence model can be plugged-in and used. As a result, our
algorithm achieves exceptional scalability and expressiveness. We evaluate our
algorithm on seven real-world datasets of both continuous-time and
discrete-time dynamic graphs. The experimental results demonstrate that our
algorithm achieves state-of-the-art performance in both kinds of dynamic
graphs. Most notably, the scalability of our algorithm is well illustrated by
its successful application to large graphs with up to over a billion temporal
edges and over a hundred million nodes.

本文提出了一种分离式图神经网络方法，适用于连续型和离散型大型动态图，通过统一的动态传播方法，能够在预测任务中支持序列模型，实现了卓越的可扩展性和表现力，实验结果在连续型和离散型动态图上均达到了最先进表现。