No Cover Image

Journal article 514 views 660 downloads

Graph Deep Learning: State of the Art and Challenges

Stavros Georgousis, Michael Kenning, Xianghua Xie Orcid Logo

IEEE Access, Volume: 9, Pages: 22106 - 22140

Swansea University Authors: Michael Kenning, Xianghua Xie Orcid Logo

  • 56088.pdf

    PDF | Version of Record

    Released under the terms of a Creative Commons Attribution 4.0 License

    Download (5.94MB)

Abstract

The last half-decade has seen a surge in deep learning research on irregular domains and efforts to extend convolutional neural networks (CNNs) to work on irregularly structured data. The graph has emerged as a particularly useful geometrical object in deep learning, able to represent a variety of i...

Full description

Published in: IEEE Access
ISSN: 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa56088
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: The last half-decade has seen a surge in deep learning research on irregular domains and efforts to extend convolutional neural networks (CNNs) to work on irregularly structured data. The graph has emerged as a particularly useful geometrical object in deep learning, able to represent a variety of irregular domains well. Graphs can represent various complex systems, from molecular structure, to computer and social and traffic networks. Consequent on the extension of CNNs to graphs, a great amount of research has been published that improves the inferential power and computational efficiency of graph- based convolutional neural networks (GCNNs).The research is incipient, however, and our understanding is relatively rudimentary. The majority of GCNNs are designed to operate with certain properties. In this survey we review of the state of graph representation learning from the perspective of deep learning. We consider challenges in graph deep learning that have been neglected in the majority of work, largely because of the numerous theoretical difficulties they present. We identify four major challenges in graph deep learning: dynamic and evolving graphs, learning with edge signals and information, graph estimation, and the generalization of graph models. For each problem we discuss the theoretical and practical issues, survey the relevant research, while highlighting the limitations of the state of the art. Advances on these challenges would permit GCNNs to be applied to wider range of domains, in situations where graph models have previously been limited owing to the obstructions to applying a model owing to the domains’ natures.
College: Faculty of Science and Engineering
Start Page: 22106
End Page: 22140