The Evolution of Distributed Systems for Graph Neural Networks and their
  Origin in Graph Processing and Deep Learning: A Survey
Jana Vatter Ruben Mayer Hans-Arno Jacobsen
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.