Estimating Fair Graphs from Graph-Stationary Data

2025-10-08

AI Summary

Biased edge connections in graph data with respect to sensitive attributes impair fairness in downstream tasks. Method: We propose FairSpecTemp, a novel method that estimates an unbiased graph structure from stationary node observations by operating in the spectral domain. It enforces fairness constraints—either explicitly via spectral-domain constraints or implicitly through regularization—and leverages graph signal exchangeability to mitigate bias. Contribution/Results: We provide theoretical guarantees that accuracy and fairness are simultaneously attainable. Extensive experiments on synthetic and real-world datasets demonstrate that both variants of FairSpecTemp efficiently recover fair graph structures, preserving reconstruction fidelity while substantially improving both group-level and individual-level fairness. Our approach establishes a new paradigm for fair representation learning on graphs.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Bias, Fairness & Privacy
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

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

We estimate fair graphs from graph-stationary nodal observations such that connections are not biased with respect to sensitive attributes. Edges in real-world graphs often exhibit preferences for connecting certain pairs of groups. Biased connections can not only exacerbate but even induce unfair treatment for downstream graph-based tasks. We therefore consider group and individual fairness for graphs corresponding to group- and node-level definitions, respectively. To evaluate the fairness of a given graph, we provide multiple bias metrics, including novel measurements in the spectral domain. Furthermore, we propose Fair Spectral Templates (FairSpecTemp), an optimization-based method with two variants for estimating fair graphs from stationary graph signals, a general model for graph data subsuming many existing ones. One variant of FairSpecTemp exploits commutativity properties of graph stationarity while directly constraining bias, while the other implicitly encourages fair estimates by restricting bias in the graph spectrum and is thus more flexible. Our methods enjoy high probability performance bounds, yielding a conditional tradeoff between fairness and accuracy. In particular, our analysis reveals that accuracy need not be sacrificed to recover fair graphs. We evaluate FairSpecTemp on synthetic and real-world data sets to illustrate its effectiveness and highlight the advantages of both variants of FairSpecTemp.

Problem

• Estimating unbiased graph connections from stationary nodal observations
• Addressing group and individual fairness in graph structure estimation
• Mitigating biased connections that harm downstream graph-based tasks

Innovation

• Estimates fair graphs from stationary nodal observations
• Uses spectral templates for bias-constrained optimization
• Ensures fairness without sacrificing graph accuracy