Ngā tūemi rite

• Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks
  mā: Zhan, Duna, me ētahi atu.
  I whakaputaina: (2024)
• FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently
  mā: Cong, Zicun, me ētahi atu.
  I whakaputaina: (2024)
• Endowing Pre-trained Graph Models with Provable Fairness
  mā: Zhang, Zhongjian, me ētahi atu.
  I whakaputaina: (2024)
• Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation
  mā: Li, Chengyu, me ētahi atu.
  I whakaputaina: (2024)
• Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks
  mā: Zhang, He, me ētahi atu.
  I whakaputaina: (2023)