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Found 4 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.
Approximating Score-based Explanation Techniques Using Conformal Regression
The results also suggest that the proposed method can produce tight intervals, while providing validity guarantees.
Interpretable Graph Neural Networks for Tabular Data
Data in tabular format is frequently occurring in real-world applications.
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