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MAPPING: debiasing graph neural networks for fair node classification with limited sensitive information leakage

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Abstract

Despite remarkable success in diverse web-based applications, Graph Neural Networks (GNNs) inherit and further exacerbate historical discrimination and social stereotypes, which critically hinder their deployments in high-stake domains such as online clinical diagnosis, financial crediting, etc. However, existing research in fair graph learning typically favors pairwise constraints to achieve fairness but fails to cast off dimensional limitations and generalize them into multiple sensitive attributes. Besides, most studies focus on in-processing techniques to enforce and calibrate fairness, constructing a model-agnostic debiasing GNN framework at the pre-processing stage to prevent downstream misuses and improve training reliability is still largely under-explored. Furthermore, previous work tends to enhance either fairness or privacy individually but few probes into how fairness issues trigger privacy concerns and whether such concerns can be alleviated with fairness intervention. In this paper, we propose a novel model-agnostic debiasing framework named MAPPING (Masking And Pruning and Message-Passing trainING) for fair node classification, in which we adopt the distance covariance (dCov)-based fairness constraints to simultaneously reduce feature and topology biases under multiple sensitive memberships, and combine them with adversarial debiasing to confine the risks of sensitive attribute inference. Experiments on real-world datasets with different GNN variants demonstrate the effectiveness and flexibility of MAPPING. Our results show that MAPPING can achieve better trade-offs between utility and fairness, and mitigate privacy risks of sensitive information leakage. This work paves the way for a new direction in trustworthy GNNs by addressing fairness and privacy concerns simultaneously, rather than achieving fairness at the expense of privacy.

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Data Availability

Data is provided within the manuscript.

Materials Availability

Yes.

Code Availability

Currently no.

Notes

  1. https://github.com/chirag126/nifty

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Acknowledgements

This research was supported in part by the University of Pittsburgh Center for Research Computing through the resources provided. The first author acknowledges the support from the SCI fellowship.

Funding

This research was supported in part by the University of Pittsburgh Center for Research Computing through the resources provided.

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Authors and Affiliations

  1. Department of Informatics and Networked Systems, University of Pittsburgh, 135 North Bellefield Avenue, Pittsburgh, 15213, PA, USA

    Ying Song & Balaji Palanisamy

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Balaji Palanisamy as an advisor reviewed and edited the manuscript. Ying Song finished all sections except for the above two parts.

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Appendices

Appendix A: Empirical analysis

1.1 A.1 Data synthesis

Fig. 7
figure 7

Distributions of biased and unbiased graph data based on the minor sensitive attribute. The minor sensitive attribute is binary, where group 0 represents the minority while group 1 denotes the majority

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Please note that \(S_m\) can be highly related to \(S_n\) or even not. For \(S_n\), we generate a \(2500 \times 3\) biased non-sensitive feature matrix from multivariate normal distributions \(\mathcal {N} \left( \mu _0, \Sigma _0\right) \) and \(\mathcal {N} \left( \mu _1, \Sigma _1\right) \), where subgroup 0 represents minority in reality, \(\mu _0=\left( -10,-2,-5\right) ^{T}\), \(\mu _1=\left( 10,2,5\right) ^{T}\), \(\Sigma _0=\Sigma _1\) are both identity matrices, and \(|S_0|=500\) and \(|S_1|=2000\). We combine the top 100 sample from \(\left( \mu _0,\Sigma _0\right) \), the top 200 from \(\left( \mu _1,\Sigma _1\right) \), then the next 200 from \(\left( \mu _0,\Sigma _0\right) \) and 600 from \(\left( \mu _1,\Sigma _1\right) \), then the next 100 from \(\left( \mu _0,\Sigma _0\right) \) and 700 from \(\left( \mu _1,\Sigma _1\right) \), and finally the last 100 from \(\left( \mu _0,\Sigma _0\right) \) and 500 from \(\left( \mu _1,\Sigma _1\right) \). Next, generate \(S_n\) based on this combination. And then we create another \(2500 \times 3\) non-sensitive matrix attached to \(S_n\) from multivariate normal distributions \(\mathcal {N} \left( \mu _2, \Sigma _2\right) \) and \(\mathcal {N} \left( \mu _3, \Sigma _3\right) \), where subgroup 2 represents minority in reality, \(\mu _2=\left( -12,-8,-4\right) ^{T}\), \(\mu _3=\left( 12,8,4\right) ^{T}\), \(\Sigma _2=\Sigma _3\) are both identity matrices, and \(|S_2|=700\) and \(|S_3|=1800\). We combine the top 300 from \(\left( \mu _2,\Sigma _2\right) \), and 1200 from \(\left( \mu _3,\Sigma _3\right) \), and then 400 from \(\left( \mu _2,\Sigma _2\right) \) and 600 from \(\left( \mu _3,\Sigma _3\right) \). Next, generate \(S_m\) based on this combination again. Debiased features are sampled from multivariate normal distributions with \(\mu _4=\left( 0,1,0,1,0,1\right) \) and covariance as the identity matrix. Second, the biased topology is formed via the stochastic block model, where the first block contains 500 nodes while the second contains 2000 nodes, and the link probability within blocks is 5e-3 and between blocks is 1e-7. The debiased topology is built with a random geometric graph with 0.033 radius.

1.2 A.2 Implementation details

We built 1-layer GCNs with PyTorch Geometric [69] with Adam optimizer [70], learning rate 1e-3, dropout 0.2, weight decay 1e-5, training epoch 1000, and hidden layer size 16 and implemented them in PyTorch [71]. All experiments are conducted on a 64-bit machine with 4 Nvidia A100 GPUs. The experiments are trained on 1, 000 epochs. We repeat experiments 10 times with different seeds to report the average results.

Algorithm 1
figure a

Pre-masking Strategies.

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Algorithm 2
figure b

MAPPING.

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Appendix B: Pseudo codes

1.1 B.1 Algorithm 1 - Pre-masking Strategies

1.2 B.2 Algorithm 2 - MAPPING

Appendix C: Experiments

1.1 C.1 Dataset description

In German, nodes represent bank clients and edges are connected based on the similarity of clients’ credit accounts. To control credit risks, the bank needs to differentiate applicants with good/bad credits. In Recidivism, nodes denote defendants who got released on bail at the U.S state courts during 1990-2009 and edges are linked based on the similarity of defendants’ basic demographics and past criminal histories. The task is to predict whether the defendants will be bailed. In Credit, nodes are credit card applicants and edges are formed based on the similarity of applicants’ spending and payment patterns. The goal is to predict whether the applicants will default.

1.2 C.2 Hyperparameter setting of SOTA

In this subsection, we detail the hyperparameters for different fair models. To obtain relatively better performance, we leverage Optuna to facilitate grid search.

FairGNN: dropout from \(\{0.1,0.2,0.3,0.4,0.5\}\), weight decay 1e-5, learning rate \(\{0.001,0.005,0.01,0.05,0.1\}\), regularization coefficients \(\alpha =4\) and \(\beta =0.01\), sensitive number 200 and label number 500, hidden layer size \(\{16,32,64,128\}\), .

NIFTY: project hidden layer size 16, drop edge and feature rates are 0.001 and 0.1, dropout \(\{0.1,0.3,0.5\}\), weight decay 1e-5, learning rate \(\{0.0001,0.001,0.01\}\), regularization coefficient \(\{0.4,0.5,0.6,0.7,0.8\}\), hidden layer size 16.

EDITS: we directly use the debiased datasets in [15], dropout \(\{0.05,0.1,0.3,0.5\}\), weight decay {1e-4,1e-5,1e-6,1e-7}, learning rate \(\{0.001,0.005,0.01,0.05\}\), hidden layer size 16.

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Song, Y., Palanisamy, B. MAPPING: debiasing graph neural networks for fair node classification with limited sensitive information leakage. World Wide Web 27, 74 (2024). https://doi.org/10.1007/s11280-024-01312-0

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  • DOI: https://doi.org/10.1007/s11280-024-01312-0

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