No confident code implementation found for this paper. 1 possible match can be shown below.
This report gives a summary of two problems about graph convolutional networks (GCNs): over-smoothing and heterophily challenges, and outlines future directions to explore.
No confident code match yet
We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.
No code implementations found yet.
Know of an implementation? Let us know in the comments below!
Beyond Over-smoothing: Uncovering the Trainability Challenges in Deep Graph Neural Networks
A Survey on Oversmoothing in Graph Neural Networks
Graphs Unveiled: Graph Neural Networks and Graph Generation
Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization
Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons
Foundations and Frontiers of Graph Learning Theory
@article{luan2023addressing,
title = {On Addressing the Limitations of Graph Neural Networks},
author = {Sitao Luan},
year = {2023},
eprint = {2306.12640},
archivePrefix = {arXiv},
url = {http://arxiv.org/abs/2306.12640v2}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/2306.12640)