FairGC: Fairness-aware Graph Condensation

Yihan Gao; Chenxi Huang; Wen Shi; Ke Sun; Ziqi Xu; Xikun Zhang; Mingliang Hou; Renqiang Luo

cs.LGMar 30, 2026

FairGC: Fairness-aware Graph Condensation

Authors: Yihan Gao, Chenxi Huang, Wen Shi, Ke Sun, Ziqi Xu, Xikun Zhang, Mingliang Hou, Renqiang Luo

Organizations: College of Computer Science and Technology, Jilin University, Changchun, China · Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Dalian, China · School of Computing Technologies, RMIT University, Melbourne, Australia · Guangdong Institute of Smart Education, Jinan University, Guangzhou, China

Abstract

Graph condensation (GC) has become a vital strategy for scaling Graph Neural Networks by compressing massive datasets into small, synthetic node sets. While current GC methods effectively maintain predictive accuracy, they are primarily designed for utility and often ignore fairness constraints. Because these techniques are bias-blind, they frequently capture and even amplify demographic disparities found in the original data. This leads to synthetic proxies that are unsuitable for sensitive applications like credit scoring or social recommendations. To solve this problem, we introduce FairGC, a unified framework that embeds fairness directly into the graph distillation process. Our approach consists of three key components. First, a Distribution-Preserving Condensation module synchronizes the joint distributions of labels and sensitive attributes to stop bias from spreading. Second, a Spectral Encoding module uses Laplacian eigen-decomposition to preserve essential global structural patterns. Finally, a Fairness-Enhanced Neural Architecture employs multi-domain fusion and a label-smoothing curriculum to produce equitable predictions. Rigorous evaluations on four real-world datasets, show that FairGC provides a superior balance between accuracy and fairness. Our results confirm that FairGC significantly reduces disparity in Statistical Parity and Equal Opportunity compared to existing state-of-the-art condensation models. The codes are available at https://github.com/LuoRenqiang/FairGC.

Figures & tables

Fig. 1: Fairness evaluation of GC methods on the Pokec-n-G dataset. Both vanilla GC methods (darker bars) and their fairness-enhanced versions integrated with FairGNN (lighter bars) exhibit worse fairness compared to our proposed FairGC. $\Delta_{\text{SP}}$ and $\Delta_{\text{EO}}$ are employed as fairness metrics (refer to Section III-B ), where lower values represent better fairness.

Fig. 2: The illustration of FairGC.

	Pokec-n-G (r: 0.05)			Pokec-n-R (r: 0.05)			Pokec-z-G (r: 0.05)
Methods	ACC(%) $\uparrow$	$\Delta_{\text{SP}}$ (%) $\downarrow$	$\Delta_{\text{EO}}$ (%) $\downarrow$	ACC(%) $\uparrow$	$\Delta_{\text{SP}}$ (%) $\downarrow$	$\Delta_{\text{EO}}$ (%) $\downarrow$	ACC(%) $\uparrow$	$\Delta_{\text{SP}}$ (%) $\downarrow$	$\Delta_{\text{EO}}$ (%) $\downarrow$
GCond (ICLR ’22)	$69.36_{\pm 0.94}$	$6.01_{\pm 2.39}$	$7.59_{\pm 3.33}$	$69.32_{\pm 0.81}$	$3.81_{\pm 0.52}$	$6.57_{\pm 1.01}$	$63.21_{\pm 1.23}$	$5.40_{\pm 1.05}$	$5.90_{\pm 1.64}$
DosCond (KDD ’22)	$68.83_{\pm 1.19}$	$5.14_{\pm 0.97}$	$8.91_{\pm 2.27}$	$68.54_{\pm 1.62}$	$6.14_{\pm 0.64}$	$6.94_{\pm 2.60}$	$64.30_{\pm 0.69}$	$5.87_{\pm 1.29}$	$5.60_{\pm 1.14}$
SFGC (NeurIPS ’23)	$70.45_{\pm 1.24}$	$5.01_{\pm 0.54}$	$9.02_{\pm 2.01}$	$69.82_{\pm 1.58}$	$3.61_{\pm 0.86}$	$1.83_{\pm 0.67}$	$63.46_{\pm 1.57}$	$1.47_{\pm 0.24}$	$2.84_{\pm 1.46}$
GCGP (ArXiv ’25)	$70.45_{\pm 0.95}$	$6.96_{\pm 0.98}$	$4.37_{\pm 0.43}$	$70.39_{\pm 2.03}$	$4.68_{\pm 0.08}$	$3.28_{\pm 0.36}$	$64.40_{\pm 0.49}$	$10.86_{\pm 0.31}$	$8.35_{\pm 0.25}$
GDCK (PAKDD ’25)	$70.13_{\pm 0.54}$	$3.01_{\pm 0.59}$	$5.61_{\pm 0.83}$	$70.31_{\pm 0.68}$	$2.52_{\pm 0.57}$	$2.97_{\pm 0.86}$	$64.48_{\pm 0.34}$	$2.47_{\pm 0.13}$	$6.30_{\pm 0.17}$

TABLE I: Experimental Results on Different Datasets.

\uparrow

denotes the larger, the better;

\downarrow

denotes the opposite. The best results are red and bold-faced . The runner-ups are blue and underlined . OOM means out-of-memory.

Dataset	Metric	FairGC	FairGC w/o C	FairGC w/o F
P-n-G	ACC (%)	$\mathbf{70.87_{\pm 0.28}}$	$70.17_{\pm 0.05}$	$69.36_{\pm 0.94}$
	$\Delta_{\text{SP}}$ (%)	$\mathbf{0.40_{\pm 0.23}}$	$0.48_{\pm 0.26}$	$6.01_{\pm 2.39}$
	$\Delta_{\text{EO}}$ (%)	$\mathbf{0.42_{\pm 0.11}}$	$1.05_{\pm 0.85}$	$7.59_{\pm 3.33}$
P-n-R	ACC (%)	$\mathbf{70.47_{\pm 0.14}}$	$70.23_{\pm 1.06}$	$69.32_{\pm 0.81}$
	$\Delta_{\text{SP}}$ (%)	$\mathbf{0.58_{\pm 0.08}}$	$0.67_{\pm 0.25}$	$3.81_{\pm 0.52}$
	$\Delta_{\text{EO}}$ (%)	$\mathbf{0.84_{\pm 0.13}}$	$1.06_{\pm 0.66}$	$6.57_{\pm 1.01}$

TABLE II: Ablation study results for FairGC. “P” and “AM” denote the Pokec and AMiner datasets, respectively.

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Graph neural networks (GNNs) frequently encounter group fairness issues, often yielding biased predictions against specific demographic groups defined by sensitive attributes such as gender or race. While this challenge has motivated extensive research, most existing solutions rely on the strong assumption that demographics are fully available. To bypass this strict requirement, a few recent studies have attempted to use predicted demographics as proxies to enforce fairness constraints. However, predicted demographics may be inaccurate, resulting in the failure to improve fairness. In this work, we investigate the problem of graph fairness without demographic information and avoid the utilization of predicted demographics. Motivated by our observation that the gradient distributions of misclassified nodes implicitly encode demographic information, we first propose GradDist, a gradient-based metric that quantifies bias by measuring the distance between local modes within these distributions. To mitigate this bias, we propose Gradient-to-Fairness (Grad2Fair), a gradient-guided approach for group fairness without demographics. Due to the potential demographics in gradients, Grad2Fair directly leverages gradients to debias and eliminates demographic prediction, thereby enabling stable fairness performance. Experiments on several real-world datasets demonstrate the effectiveness of Grad2Fair, as evidenced by superior performance over baselines in most cases. Our code is available at https://github.com/ZzoomD/Grad2Fair.

Abstract

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