There has been significant progress in improving the performance of graph neural networks (GNNs) through enhancements in graph data, model architecture design, and training strategies. For fairness in graphs, recent studies achieve fair representations and predictions through either graph data pre-processing (e.g., node feature masking, and topology rewiring) or fair training strategies (e.g., regularization, adversarial debiasing, and fair contrastive learning). How to achieve fairness in graphs from the model architecture perspective is less explored. More importantly, GNNs exhibit worse fairness performance compared to multilayer perception since their model architecture (i.e., neighbor aggregation) amplifies biases. To this end, we aim to achieve fairness via a new GNN architecture. We propose Fair Message Passing (FMP) designed within a unified optimization framework for GNNs. Notably, FMP explicitly renders sensitive attribute usage in forward propagation for node classification task using cross-entropy loss without data pre-processing. In FMP, the aggregation is first adopted to utilize neighbors' information and then the bias mitigation step explicitly pushes demographic group node presentation centers together.In this way, FMP scheme can aggregate useful information from neighbors and mitigate bias to achieve better fairness and prediction tradeoff performance. Experiments on node classification tasks demonstrate that the proposed FMP outperforms several baselines in terms of fairness and accuracy on three real-world datasets. The code is available at https://github.com/zhimengj0326/FMP.
FairEGM: Fair Link Prediction and Recommendation via Emulated Graph Modification
As machine learning becomes more widely adopted across domains, it is critical that researchers and ML engineers think about
the inherent biases in the data that may be perpetuated by the model. Recently, many studies have shown that such biases are
also imbibed in Graph Neural Network (GNN) models if the input graph is biased, potentially to the disadvantage of underserved
and underrepresented communities. In this work, we aim to mitigate the bias learned by GNNs by jointly optimizing two different
loss functions: one for the task of link prediction and one for the task of demographic parity. We further implement three different
techniques inspired by graph modification approaches: the Global Fairness Optimization (GFO), Constrained Fairness Optimization
(CFO), and Fair Edge Weighting (FEW) models. These techniques mimic the effects of changing underlying graph structures within
the GNN and offer a greater degree of interpretability over more integrated neural network methods. Our proposed models emulate
microscopic or macroscopic edits to the input graph while training GNNs and learn node embeddings that are both accurate and fair
under the context of link recommendations. We demonstrate the effectiveness of our approach on four real world datasets and show
that we can improve the recommendation fairness by several factors at negligible cost to link prediction accuracy.
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- NSF-PAR ID:
- 10355932
- Date Published:
- Journal Name:
- ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
