Fairness-Aware Node Representation Learning
- URL: http://arxiv.org/abs/2106.05391v1
- Date: Wed, 9 Jun 2021 21:12:14 GMT
- Title: Fairness-Aware Node Representation Learning
- Authors: \"Oyk\"u Deniz K\"ose, Yanning Shen
- Abstract summary: This study addresses fairness issues in graph contrastive learning with fairness-aware graph augmentation designs.
Different fairness notions on graphs are introduced, which serve as guidelines for the proposed graph augmentations.
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.
- Score: 9.850791193881651
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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