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Found 6 papers, 0 papers with code
FairWire: Fair Graph Generation
Faced with the bias amplification in graph generation models that is brought to light in this work, we further propose a fair graph generation framework, FairWire, by leveraging our fair regularizer design in a generative model.
Fairness-aware Optimal Graph Filter Design
We show that the optimal design of said filters can be cast as a convex problem in the graph spectral domain.
FairGAT: Fairness-aware Graph Attention Networks
Although it is shown that the use of graph structures in learning results in the amplification of algorithmic bias, the influence of the attention design in GATs on algorithmic bias has not been investigated.
Fairness-Aware Graph Filter Design
Graphs are mathematical tools that can be used to represent complex real-world systems, such as financial markets and social networks.
FairNorm: Fair and Fast Graph Neural Network Training
In addition, it is empirically shown that the proposed framework leads to faster convergence compared to the naive baseline where no normalization is employed.
Fair Node Representation Learning via Adaptive Data Augmentation
Our analysis reveals that both nodal features and graph structure lead to bias in the obtained representations.
