Target-Aware Interaction-Guided Reinforcement Learning for Black-Box Node Injection Attacks on Graph Neural Networks
Graph Neural Networks (GNNs) have achieved remarkable performance in graph representation learning, yet their inherent vulnerability to adversarial attacks poses severe security risks. Especially, black-box node injection attacks have become a major threat to GNNs since they inject malicious nodes without altering the original graph topology. However, they typically decouple the generation of malicious node features and edge connections, thereby resulting in suboptimal attack efficacy under stringent budgets. To address this critical issue, this study proposes a novel Target-aware Interaction-guided Reinforcement learning for Black-box node injection Attacks on GNNs (TIRBA), which formula...
| Authors | Yi Lan, Ye Yuan |
|---|---|
| Primary category | cs.LG |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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