FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networks

2026-01-14

AI Summary

This work addresses the challenge of fairness in graph representation learning when sensitive attributes are missing in social networks. Existing graph Transformer methods often reconstruct sensitive attributes, inadvertently introducing bias and risking privacy leakage. To overcome this, the authors propose FairGE, a novel framework that encodes fairness directly through spectral graph theory without generating sensitive attributes. FairGE leverages the leading eigenvectors of the graph Laplacian to capture structural information and employs zero-padding for missing attributes to preserve independence. This approach effectively mitigates bias amplification and privacy concerns. Extensive experiments on seven real-world datasets demonstrate that FairGE improves both statistical parity and equal opportunity by at least 16% on average compared to state-of-the-art baselines.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Bias, Fairness & Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Graph Transformers (GTs) are increasingly applied to social network analysis, yet their deployment is often constrained by fairness concerns. This issue is particularly critical in incomplete social networks, where sensitive attributes are frequently missing due to privacy and ethical restrictions. Existing solutions commonly generate these incomplete attributes, which may introduce additional biases and further compromise user privacy. To address this challenge, FairGE (Fair Graph Encoding) is introduced as a fairness-aware framework for GTs in incomplete social networks. Instead of generating sensitive attributes, FairGE encodes fairness directly through spectral graph theory. By leveraging the principal eigenvector to represent structural information and padding incomplete sensitive attributes with zeros to maintain independence, FairGE ensures fairness without data reconstruction. Theoretical analysis demonstrates that the method suppresses the influence of non-principal spectral components, thereby enhancing fairness. Extensive experiments on seven real-world social network datasets confirm that FairGE achieves at least a 16% improvement in both statistical parity and equality of opportunity compared with state-of-the-art baselines. The source code is shown in https://github.com/LuoRenqiang/FairGE.

Problem

• fairness
• incomplete social networks
• Graph Transformers
• sensitive attributes
• privacy

Innovation

• Fair Graph Encoding
• Graph Transformers
• Spectral Graph Theory
• Fairness-aware Learning
• Incomplete Social Networks