Mitigating Subpopulation Bias for Fair Network Topology Inference

2024/03/22 by Madeline Navarro, Navarro, Madeline, Samuel Rey +7 · 3 citations
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #FOS: Electrical engineering #Opinion Dynamics and Social Influence #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2403.15591

openalex publication_date 2024/03/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We consider fair network topology inference from nodal observations. Real-world networks often exhibit biased connections based on sensitive nodal attributes. Hence, different subpopulations of nodes may not share or receive information equitably. We thus propose an optimization-based approach to accurately infer networks while discouraging biased edges. To this end, we present bias metrics that measure topological demographic parity to be applied as convex penalties, suitable for most optimization-based graph learning methods. Moreover, we encourage equitable treatment for any number of subpopulations of differing sizes. We validate our method on synthetic and real-world simulations using networks with both biased and unbiased connections.

Cited by

• Estimating Fair Graphs from Graph-Stationary Data
• Adaptive Node Feature Selection For Graph Neural Networks
• FAROS: Fair Graph Generation via Attribute Switching Mechanisms

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