Brief Report
Version 1
Preserved in Portico This version is not peer-reviewed
On Addressing the Limitations of Graph Neural Networks
Version 1
: Received: 2 July 2023 / Approved: 3 July 2023 / Online: 4 July 2023 (03:28:51 CEST)
How to cite: Luan, S. On Addressing the Limitations of Graph Neural Networks. Preprints 2023, 2023070118. https://doi.org/10.20944/preprints202307.0118.v1 Luan, S. On Addressing the Limitations of Graph Neural Networks. Preprints 2023, 2023070118. https://doi.org/10.20944/preprints202307.0118.v1
Abstract
This report gives a comprehensive summary of two problems about graph convolutional networks (GCNs): over-smoothing and heterophily challenges, and outlines future directions to explore.
Keywords
Graph Neural Networks; Reinforcement Learning; Over-smoothing; Heterophily
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment