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GADD: Game-Inspired Adversarial Distillation for Robust Graph Defense

Graph neural networks (GNNs) are highly effective on relational data, yet their performance degrades sharply when graph topology is poisoned before training. Existing defenses usually assume a fixed attack pattern and a fixed graph structure, which makes them brittle when the poisoned graph changes...

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Autores principales: Yabin Peng, Chenyu Zhou, Yuchen Liu, Kunlin Li, Fan Zhang, Shaoxun Liu
Formato: Artigo
Lenguaje:Inglês
Publicado: MDPI AG 2026-05-01
Colección:Information
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Acceso en línea:https://www.mdpi.com/2078-2489/17/6/527
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