Blackknife: Query-Free Black-Box Rewiring Attacks on Heterogeneous Graph Neural Networks
Heterogeneous graph neural networks (HGNNs) perform well on graphs with multiple node and relation types, but their robustness to attacks with restricted structural access remains insufficiently understood. We propose Blackknife, a query-free, strict black-box evasion attack that observes only the one-hop structures of the attacked nodes, their ground-truth labels, and raw features within the permitted action space. No victim-model outputs are used to train the surrogate, generate perturbations, or select attacks.Blackknife trains a shared surrogate using only observable edges and relation types, then optimizes structural perturbations through continuous relaxation and projected gradient ...
| Authors | Honglin Gao, Junhao Ren, Lan Zhao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 28 June 2026 |
| Last updated at source | 14 October 2026 |
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