Survey on Graph Neural Networks

Georgios Gkarmpounis, Christos Vranis, Nicholas Vretos, Petros Daras

Abstract: Graph Neural Networks (GNNs) have become a powerful tool in order to learn from graph-structured data. Their ability to capture complex relationships and dependencies within graph structures, allows them to have a great number of applications in various domains, including social network analysis, recommendation systems and drug discovery. The aim of this work is to provide a detailed overview of the models in Graph Neural Networks and propose a new taxonomy of GNNs, including Deep Generative Models for graphs as a distinct category. The works included were selected based on their impact, with recent related papers also considered.