Graph Neural Networks (GNNs) are an emerging research field. This specialized Deep Neural Network (DNN) architecture is capable of processing graph structured data and bridges the gap between graph processing and Deep Learning (DL). As graphs are everywhere, GNNs can be applied to various domains including recommendation systems, computer vision, natural language processing, biology and chemistry. With the rapid growing size of real world graphs, the need for efficient and scalable GNN training solutions has come. Consequently, many works proposing GNN systems have emerged throughout the past few years. However, there is an acute lack of overview, categorization and comparison of such systems. We aim to fill this gap by summarizing and categorizing important methods and techniques for large-scale GNN solutions. In addition, we establish connections between GNN systems, graph processing systems and DL systems.

翻译：图神经网络（GNN）是一个新兴的研究领域。这种专用深度神经网络（DNN）架构能够处理图结构数据，并填补了图处理与深度学习（DL）之间的鸿沟。由于图结构普遍存在于各类场景中，GNN可应用于推荐系统、计算机视觉、自然语言处理、生物学和化学等多个领域。随着现实世界图数据规模的快速增长，对高效且可扩展的GNN训练方案的需求应运而生。近年来，众多提出GNN系统的工作相继涌现。然而，目前严重缺乏对此类系统的系统梳理、分类与对比。我们旨在通过总结和归类大规模GNN解决方案的重要方法与技术来填补这一空白。此外，我们还将建立GNN系统、图处理系统与深度学习系统之间的关联。