In this work, we propose the first backdoor attack to graph neural networks (GNN). Specifically, we propose a subgraph based backdoor attack to GNN for graph classification. In our backdoor attack, a GNN classifier predicts an attacker-chosen target ...
Graph Neural Networks (GNNs) have achieved promising results in various tasks such as node classification and graph classification. Recent studies find that GNNs are vulnerable to adversarial attacks. However, effective backdoor attacks on graphs are ...
Graph Neural Networks (GNNs) are a class of deep learning-based methods for processing graph domain information. GNNs have recently become a widely used graph analysis method due to their superior ability to learn representations for complex graph ...
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