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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| Автори: | , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Tsinghua University Press
2024-03-01
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| Серія: | Big Data Mining and Analytics |
| Предмети: | |
| Онлайн доступ: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020019 |
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