Methods › General › Adversarial Attacks › G-NIA
Generalizable Node Injection Attack
G-NIA
Introduced by Shuchang Tao et al. in Single Node Injection Attack against Graph Neural Networks
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Generalizable Node Injection Attack, or G-NIA, is an attack scenario for graph neural networks where the attacker injects malicious nodes rather than modifying original nodes or edges to affect the performance of GNNs. G-NIA generates the discrete edges also by Gumbel-Top-𝑘 following OPTI and captures the coupling effect between network structure and node features by a sophisticated designed model.
G-NIA explicitly models the most critical feature propagation via jointly modeling. Specifically, the malicious attributes are adopted to guide the generation of edges, modeling the influence of attributes and edges. G-NIA also adopts a model-based framework, utilizing useful information of attacking during model training, as well as saving computational cost during inference without re-optimization.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Single Node Injection Attack against Graph Neural Networks 30 Aug 2021 · 1 repository · arXiv:2108.13049Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
Tasks archive 2025-07-28
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Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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