The Evolution of Distributed Systems for Graph Neural Networks and Their Origin in Graph Processing and Deep Learning: A Survey

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Abstract

Graph neural networks (GNNs) are an emerging research field. This specialized deep neural network architecture is capable of processing graph structured data and bridges the gap between graph processing and deep learning. 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. Additionally, we establish connections between GNN systems, graph processing systems, and deep learning systems.

Original languageEnglish
Article number6
JournalACM Computing Surveys
Volume56
Issue number1
DOIs
StatePublished - 28 Aug 2023
Externally publishedYes

Keywords

  • Additional Key Words and PhrasesGraph neural networks
  • deep learning systems
  • graph processing systems

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