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Found 5 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.
TEGDetector: A Phishing Detector that Knows Evolving Transaction Behaviors
Phishing detectors direct their efforts in hunting phishing addresses.
Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction
Backdoor attacks induce the DLP methods to make wrong prediction by the malicious training data, i. e., generating a subgraph sequence as the trigger and embedding it to the training data.
EGC2: Enhanced Graph Classification with Easy Graph Compression
To achieve lower-complexity defense applied to graph classification models, EGC2 utilizes a centrality-based edge-importance index to compress the graphs, filtering out trivial structures and adversarial perturbations in the input graphs, thus improving the model's robustness.
