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Found 4 papers, 3 papers with code
Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs
Most of the proposed studies launch the backdoor attack using a trigger that either is the randomly generated subgraph (e. g., erd\H{o}s-r\'enyi backdoor) for less computational burden, or the gradient-based generative subgraph (e. g., graph trojaning attack) to enable a more effective attack.
Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection
Consequently, the link prediction model trained on the backdoored dataset will predict the link with trigger to the target state.
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning
This is the first study of adversarial attacks on GVFL.
Graphfool: Targeted Label Adversarial Attack on Graph Embedding
To the best of our knowledge, this is the first targeted label attack technique.
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