A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

Documenti analoghi

• Stealing Training Graphs from Graph Neural Networks
  di: Lin, Minhua, et al.
  Pubblicazione: (2024)
• Robustness Inspired Graph Backdoor Defense
  di: Zhang, Zhiwei, et al.
  Pubblicazione: (2024)
• Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective
  di: Zhang, Zhiwei, et al.
  Pubblicazione: (2024)
• Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks
  di: Lin, Minhua, et al.
  Pubblicazione: (2024)
• Comprehensive Vulnerability Analysis is Necessary for Trustworthy LLM-MAS
  di: He, Pengfei, et al.
  Pubblicazione: (2025)