Adversarial attacks on graph classification via Bayesian optimisation
Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks. While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated t...
| Main Authors: | , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Neural Information Processing Systems Foundation
2021
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