Fairness-Aware Node Representation Learning
This addresses fairness for social network applications, but it is incremental as it builds on existing contrastive learning methods.
The paper tackles fairness issues in graph contrastive learning by proposing fairness-aware graph augmentations, such as adaptive feature masking and edge deletion, which enhance fairness metrics like statistical parity and equal opportunity while maintaining comparable classification accuracy to state-of-the-art methods.
Node representation learning has demonstrated its effectiveness for various applications on graphs. Particularly, recent developments in contrastive learning have led to promising results in unsupervised node representation learning for a number of tasks. Despite the success of graph contrastive learning and consequent growing interest, fairness is largely under-explored in the field. To this end, this study addresses fairness issues in graph contrastive learning with fairness-aware graph augmentation designs, through adaptive feature masking and edge deletion. In the study, different fairness notions on graphs are introduced, which serve as guidelines for the proposed graph augmentations. Furthermore, theoretical analysis is provided to quantitatively prove that the proposed feature masking approach can reduce intrinsic bias. Experimental results on real social networks are presented to demonstrate that the proposed augmentations can enhance fairness in terms of statistical parity and equal opportunity, while providing comparable classification accuracy to state-of-the-art contrastive methods for node classification.