Summary of Certifying Robustness Of Graph Convolutional Networks For Node Perturbation with Polyhedra Abstract Interpretation, by Boqi Chen et al.
Certifying Robustness of Graph Convolutional Networks for Node Perturbation with Polyhedra Abstract Interpretation
by Boqi Chen, Kristóf Marussy, Oszkár Semeráth, Gunter Mussbacher, Dániel Varró
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Formal Languages and Automata Theory (cs.FL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an improved technique for certifying the robustness of graph convolutional neural networks (GCNs) against node feature perturbations. GCNs are vulnerable to small perturbations in the input graph, making them susceptible to input faults or adversarial attacks. To tackle this issue, the authors introduce a novel polyhedra-based abstract interpretation approach that provides tight upper and lower bounds for the robustness of the GCN. The proposed method can be used during training to further improve the robustness of GCNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computer networks called Graph Convolutional Neural Networks (GCNs) more secure. Right now, these networks are easy to trick into giving wrong answers if someone makes small changes to the data they’re looking at. This is a big problem because we want these networks to be able to make good decisions even when things get tricky. The authors of this paper have come up with a new way to check how well GCNs will do under different conditions, and it’s better than what we had before. |
Keywords
» Artificial intelligence » Gcn
