Preprint Brief Report Version 1 Preserved in Portico This version is not peer-reviewed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

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