Many emerging user-facing services adopt Graph Neural Networks (GNNs) to
improve serving accuracy. When the graph used by a GNN model changes,
representations (embedding) of nodes in the graph should be updated
accordingly. However, the node representation update is too slow, resulting in
either long response latency of user queries (the inference is performed after
the update completes) or high staleness problem (the inference is performed
based on stale data). Our in-depth analysis shows that the slow update is
mainly due to neighbor explosion problem in graphs and duplicated computation.
Based on such findings, we propose STAG, a GNN serving framework that enables
low latency and low staleness of GNN-based services. It comprises a
collaborative serving mechanism and an additivity-based incremental propagation
strategy. With the collaborative serving mechanism, only part of node
representations are updated during the update phase, and the final
representations are calculated in the inference phase. It alleviates the
neighbor explosion problem. The additivity-based incremental propagation
strategy reuses intermediate data during the update phase, eliminating
duplicated computation problem. Experimental results show that STAG accelerates
the update phase by 1.3x~90.1x, and greatly reduces staleness time with a
slight increase in response latency.

基于发现的结果，我们提出了 STAG，这是一个 GNN 服务框架，它能够实现 GNN 服务的低延迟和低陈旧度。它包括协作服务机制和基于可加性的增量传播策略。

STAG：通过动态图实现 GNN-based 服务的低延迟和低陈旧度

STAG: Enabling Low Latency and Low Staleness of GNN-based Services with  Dynamic Graphs

Spectral Toolkit of Algorithms for Graphs (STAG) is an open-source library
for efficient spectral graph algorithms, and its development starts in
September 2022. We have so far finished the component on local graph
clustering, and this technical report presents a user's guide to STAG, showcase
studies, and several technical considerations behind our development.

STAG 是一个开源图形算法库，用于高效计算谱图算法，本技术报告介绍了 STAG 的用户指南、案例和技术考虑，目前已经完成了局部图聚类的组件。