ATPF: An Adaptive Temporal Perturbation Framework for Adversarial Attacks on Temporal Knowledge Graph

Robustness is paramount for ensuring the reliability of knowledge graph models in safety-sensitive applications. While recent research has delved into adversarial attacks on static knowledge graph models, the exploration of more practical temporal knowledge graphs has been largely overlooked. To fil...

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Veröffentlicht in:	IEEE transactions on knowledge and data engineering 2025-03, Vol.37 (3), p.1-14
Hauptverfasser:	Liao, Longquan, Zheng, Linjiang, Shang, Jiaxing, Li, Xu, Chen, Fengwen
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation models Adversarial attack Closed box deep learning dynamic network Heuristic algorithms Knowledge graphs Noise Perturbation methods Predictive models Representation learning Robustness temporal knowledge graph Timing
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