Limits of Depth: Over-Smoothing and Over-Squashing in GNNs

Graph Neural Networks (GNNs) have become a widely used tool for learning and analyzing data on graph structures, largely due to their ability to preserve graph structure and properties via graph representation learning. However, the effect of depth on the performance of GNNs, particularly isotropic...

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Bibliografiske detaljer

Principais autores:	Aafaq Mohi ud din, Shaima Qureshi
Format:	Artigo
Sprog:	Inglês
Udgivet:	Tsinghua University Press 2024-03-01
Serier:	Big Data Mining and Analytics
Fag:	graph neural networks (gnns) learning on graphs over-smoothing over-squashing isotropic-gnns anisotropic-gnns
Online adgang:	https://www.sciopen.com/article/10.26599/BDMA.2023.9020019
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