Fair Graph Neural Network with Supervised Contrastive Regularization

2024-04-09 · Mahdi Tavassoli Kejani, Fadi Dornaika, Jean-Michel Loubes

In recent years, Graph Neural Networks (GNNs) have made significant advancements, particularly in tasks such as node classification, link prediction, and graph representation. However, challenges arise from biases that can be hidden not only in the node attributes but also in the connections between entities. Therefore, ensuring fairness in graph neural network learning has become a critical problem. To address this issue, we propose a novel model for training fairness-aware GNN, which enhances the Counterfactual Augmented Fair Graph Neural Network Framework (CAF). Our approach integrates Supervised Contrastive Loss and Environmental Loss to enhance both accuracy and fairness. Experimental validation on three real datasets demonstrates the superiority of our proposed model over CAF and several other existing graph-based learning methods.

📄 PDF Abstract BibTeX arXiv:2404.06090

Code (0)

등록된 구현이 없습니다.

Tasks

counterfactualFairness Graph Neural Network Link Prediction Node Classification

Methods 이 논문이 사용한 방법론

Graph Neural Network 설명 없음

Supervised Contrastive Loss 설명 없음

Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering

2024-02-16 · Siamak Ghodsi, Seyed Amjad Seyedi, Eirini Ntoutsi

Conventional fair graph clustering methods face two primary challenges: i) They prioritize balanced clusters at the expense of cluster cohesion by imposing rigid constraints, ii) Existing methods of both individual and g…

Clustering Fairness Graph Clustering graph partitioning

Fairness-Aware Node Representation Learning

2021-06-09 · Öykü Deniz Köse, Yanning Shen

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 representat…

Contrastive Learning Fairness Node Classification Representation Learning

Fair Contrastive Learning for Facial Attribute Classification

2022-03-30 · CVPR 2022 1 · Sungho Park, Jewook Lee, Pilhyeon Lee, Sunhee Hwang 외

Learning visual representation of high quality is essential for image classification. Recently, a series of contrastive representation learning methods have achieved preeminent success. Particularly, SupCon outperformed …

Attribute Classification Contrastive Learning Facial Attribute Classification+5

Uncovering the Structural Fairness in Graph Contrastive Learning

2022-10-06 · Ruijia Wang, Xiao Wang, Chuan Shi, Le Song

Recent studies show that graph convolutional network (GCN) often performs worse for low-degree nodes, exhibiting the so-called structural unfairness for graphs with long-tailed degree distributions prevalent in the real …

Contrastive Learning Fairness

FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition

2025-06-12 · Jongsuk Kim, Jaemyung Yu, Minchan Kwon, Junmo Kim

Large-scale ASR models have achieved remarkable gains in accuracy and robustness. However, fairness issues remain largely unaddressed despite their critical importance in real-world applications. In this work, we introdu…

Automatic Speech Recognition Contrastive Learning Fairness speech-recognition+1