G-NIA
Generalizable Node Injection Attack
**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.
Single Node Injection Attack against Graph Neural Networks
https://paperswithcode.com/paper/single-node-injection-attack-against-graph
Single Node Injection Attack against Graph Neural Networks