Graph Neural Networks (GNNs) have demonstrated effectiveness in various
graph-based tasks. However, their inefficiency in training and inference
presents challenges for scaling up to real-world and large-scale graph
applications. To address the critical challenges, a range of algorithms have
been proposed to accelerate training and inference of GNNs, attracting
increasing attention from the research community. In this paper, we present a
systematic review of acceleration algorithms in GNNs, which can be categorized
into three main topics based on their purpose: training acceleration, inference
acceleration, and execution acceleration. Specifically, we summarize and
categorize the existing approaches for each main topic, and provide detailed
characterizations of the approaches within each category. Additionally, we
review several libraries related to acceleration algorithms in GNNs and discuss
our Scalable Graph Learning (SGL) library. Finally, we propose promising
directions for future research. A complete summary is presented in our GitHub
repository:
this https URL

图神经网络 (GNN) 在各种基于图的任务中显示出有效性，然而他们在训练和推理中的低效性给实现规模化的实际世界和大规模图应用带来挑战。为了解决这些关键问题，提出了一系列加速 GNN 训练和推理的算法，吸引了研究界的越来越多的关注。本文对 GNN 中的加速算法进行了系统综述，根据目的将其划分为三个主要主题：训练加速、推理加速和执行加速。具体来说，我们总结和分类了每个主题下的现有方法，并对每个类别的方法进行了详细的描述和特征化分析。此外，我们还回顾了几个与 GNN 加速算法相关的库，并讨论了我们的可扩展图学习 (SGL) 库。最后，我们提出了未来研究的有希望的方向。在我们的 GitHub 仓库中提供了完整的摘要：this https URL。