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Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks
FairGI employs the similarity matrix of individuals to achieve individual fairness within groups, while leveraging adversarial learning to address group fairness in terms of both Equal Opportunity and Statistical Parity.
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