Research Paper#Graph Neural Networks, Security, Backdoor Attacks🔬 ResearchAnalyzed: Jan 3, 2026 06:28
HeteroHBA: Backdoor Attack on Heterogeneous Graphs
Published:Dec 31, 2025 06:38
•1 min read
•ArXiv
Analysis
This paper addresses the vulnerability of Heterogeneous Graph Neural Networks (HGNNs) to backdoor attacks. It proposes a novel generative framework, HeteroHBA, to inject backdoors into HGNNs, focusing on stealthiness and effectiveness. The research is significant because it highlights the practical risks of backdoor attacks in heterogeneous graph learning, a domain with increasing real-world applications. The proposed method's performance against existing defenses underscores the need for stronger security measures in this area.
Key Takeaways
- •Proposes HeteroHBA, a generative backdoor framework for heterogeneous graphs.
- •Focuses on stealthiness by aligning trigger feature distribution with benign statistics using AdaIN and MMD loss.
- •Achieves higher attack success than baselines while maintaining clean accuracy.
- •Highlights the vulnerability of HGNNs and the need for stronger defenses.
Reference
“HeteroHBA consistently achieves higher attack success than prior backdoor baselines with comparable or smaller impact on clean accuracy.”