The field of graph representation learning is moving towards developing more robust and fair models. Recent works have focused on improving the fairness of graph neural networks, particularly in scenarios where sensitive attributes are incomplete or missing. Additionally, there is a growing interest in leveraging topological and geometric information to enhance graph representation learning. Methods such as contrastive learning, hierarchical topological granularity, and hyperbolic continuous structural entropy have shown promising results in capturing meaningful graph representations. Noteworthy papers include An Information Geometric Approach to Fairness With Equalized Odds Constraint, which proposes a novel approach to fair mechanism design using information geometry, and HTG-GCL, which introduces a framework for graph contrastive learning that leverages hierarchical topological granularity. Model-Agnostic Fairness Regularization for GNNs with Incomplete Sensitive Information is also notable for its contribution to fairness-aware graph neural networks.
Advances in Fairness and Graph Representation Learning
Sources
An Information Geometric Approach to Fairness With Equalized Odds Constraint
Adversarial Signed Graph Learning with Differential Privacy
Hyperbolic Continuous Structural Entropy for Hierarchical Clustering
Graph Data Augmentation with Contrastive Learning on Covariate Distribution Shift
HTG-GCL: Leveraging Hierarchical Topological Granularity from Cellular Complexes for Graph Contrastive Learning
Cross-View Topology-Aware Graph Representation Learning
FGC-Comp: Adaptive Neighbor-Grouped Attribute Completion for Graph-based Anomaly Detection
Model-Agnostic Fairness Regularization for GNNs with Incomplete Sensitive Information
QoSDiff: An Implicit Topological Embedding Learning Framework Leveraging Denoising Diffusion and Adversarial Attention for Robust QoS Prediction