Search Results for author:
Found 2 papers, 2 papers with code
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks
In this work, we theoretically define the concept of expected robustness in the context of attributed graphs and relate it to the classical definition of adversarial robustness in the graph representation learning literature.
A Simple and Yet Fairly Effective Defense for Graph Neural Networks
Successful combinations of our NoisyGNN approach with existing defense techniques demonstrate even further improved adversarial defense results.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
