Attacks on Node Attributes in Graph Neural Networks
CoRR(2024)
摘要
Graphs are commonly used to model complex networks prevalent in modern social
media and literacy applications. Our research investigates the vulnerability of
these graphs through the application of feature based adversarial attacks,
focusing on both decision-time attacks and poisoning attacks. In contrast to
state-of-the-art models like Net Attack and Meta Attack, which target node
attributes and graph structure, our study specifically targets node attributes.
For our analysis, we utilized the text dataset Hellaswag and graph datasets
Cora and CiteSeer, providing a diverse basis for evaluation. Our findings
indicate that decision-time attacks using Projected Gradient Descent (PGD) are
more potent compared to poisoning attacks that employ Mean Node Embeddings and
Graph Contrastive Learning strategies. This provides insights for graph data
security, pinpointing where graph-based models are most vulnerable and thereby
informing the development of stronger defense mechanisms against such attacks.
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