Generating graph perturbations to enhance the generalization of GNNs
Graph neural networks (GNNs) have become the standard approach for performing machine learning on graphs. Such models need large amounts of training data, however, in several graph classification and regression tasks, only limited training data is available. Unfortunately, due to the complex nature...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2024-01-01
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| Series: | AI Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666651024000184 |
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