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Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage

Graph Neural Networks (GNNs) have shown great power in learning node representations on graphs. However, they may inherit historical prejudices from training data, leading to discriminatory bias in predictions. Although some work has developed fair GNNs, most of them directly borrow fair representation learning techniques from non-graph domains without considering the potential problem of sensitive attribute leakage caused by feature propagation in GNNs. However, we empirically observe that feature propagation could vary the correlation of previously innocuous non-sensitive features to the sensitive ones. This can be viewed as a leakage of sensitive information which could further exacerbate discrimination in predictions. Thus, we design two feature masking strategies according to feature correlations to highlight the importance of considering feature propagation and correlation variation in alleviating discrimination. Motivated by our analysis, we propose Fair View Graph Neural Network (FairVGNN) to generate fair views of features by automatically identifying and masking sensitive-correlated features considering correlation variation after feature propagation. Given the learned fair views, we adaptively clamp weights of the encoder to avoid using sensitive-related features. Experiments on real-world datasets demonstrate that FairVGNN enjoys a better trade-off between model utility and fairness. Our code is publicly available at


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1. Introduction

As the world becomes more connected, graph mining is playing a crucial role in many domains such as drug discovery and recommendation system (Fan et al., 2019; Chen et al., 2021; Rozemberczki et al., 2022). As one of its major branches, learning informative node representation is a fundamental solution to many real-world problems such as node classification and link prediction (Wang and Derr, 2021; Zhang and Chen, 2018). Numerous data-driven models have been developed for learning node representations, among which Graph Neural Networks (GNNs) have achieved unprecedented success owing to the combination of neural networks and feature propagation (Kipf and Welling, 2017; Klicpera et al., 2019; Liu et al., 2020). Despite the significant progress of GNNs in capturing higher-order neighborhood information  (Chen et al., 2020), leveraging multi-hop dependencies (Wang and Derr, 2021), and recognizing complex local topology contexts (Wijesinghe and Wang, 2022), predictions of GNNs have been demonstrated to be unfair and perpetuate undesirable discrimination (Dai and Wang, 2021; Shumovskaia et al., 2021; Xu et al., 2021; Agarwal et al., 2021; Bose and Hamilton, 2019).

Recent studies have revealed that historical data may include previous discriminatory decisions dominated by sensitive features (Mehrabi et al., 2021; Du et al., 2020). Thus, node representations learned from such data may explicitly inherit the existing societal biases and hence exhibit unfairness when applied in practice. Besides the sensitive features, network topology also serves as an implicit source of societal bias (Dong et al., 2022a; Dai and Wang, 2021). By the principle of network homophily (McPherson et al., 2001), nodes with similar sensitive features tend to form closer connections than dissimilar ones. Since feature propagation smooths representations of neighboring nodes while separating distant ones, representations of nodes in different sensitive groups are further segregated and their corresponding predictions are unavoidably over-associated with sensitive features.

Besides above topology-induced bias, feature propagation could introduce another potential issue, termed as the sensitive information leakage. Since feature propagation naturally allows feature interactions among neighborhoods, the correlation between two feature channels is likely to vary after feature propagation, which is termed as correlation variation. As such, some original innocuous feature channels that have lower correlation to sensitive channels and encode less sensitive information may become highly correlated to sensitive ones after feature propagation and hence encode more sensitive information, which is termed as sensitive attribute leakage. Some research efforts have been invested in alleviating discrimination made by GNNs. However, they either borrow approaches from traditional fair representation learning such as adversarial debiasing (Dai and Wang, 2021; Bose and Hamilton, 2019) and contrastive learning (Köse and Shen, 2021) or directly debiasing node features and graph topology (Dong et al., 2022a; Agarwal et al., 2021) while overlooking the sensitive attribute leakage caused by correlation variation.

In this work, we study a novel and detrimental phenomenon where feature propagation can vary feature correlations and cause the leakage of sensitive information to innocuous features. To address this issue, we propose a principled framework Fair View Graph Neural Network (FairVGNN) to effectively learn fair node representations and avoid sensitive attribute leakage. Our major contributions are as follows:

  • Problem: We investigate the novel phenomenon that feature propagation could vary feature correlations and cause sensitive attribute leakage to innocuous feature channels, which could further exacerbate discrimination in predictions.

  • Algorithm: To prevent sensitive attribute leakage, we propose a novel framework FairVGNN to automatically learn fair views by identifying and masking sensitive-correlated channels and adaptively clamping weights to avoid leveraging sensitive-related features in learning fair node representations.

  • Evaluation: We perform experiments on real-world datasets to corroborate that FairVGNN can approximate the model utility while reducing discrimination.

Section 2 introduces preliminaries. In Section 3, we formally introduce the phenomenon of correlation variation and sensitive attribute leakage in GNNs and design two feature masking strategies to highlight the importance of circumventing sensitive attribute leakage for alleviating discrimination. To automatically identify/mask sensitive-relevant features, we propose FairVGNN in Section 4, which consists of a generative adversarial debiasing module to prevent sensitive attribute leakage from the input perspective by learning fair feature views and an adaptive weight clamping module to prevent sensitive attribute leakage from the model perspective by clamping weights of sensitive-correlated channels of the encoder. In Section 5, we evaluate FairVGNN by performing extensive experiments. Related work is presented in Section 6. Finally, we conclude and discuss future work in Section 7.

2. Preliminaries

2.1. Notations

We denote an attributed graph by where is the set of nodes with specifying their labels, is the set of edges with being the edge between nodes and , and is the node feature matrix with indicating the features of node , indicating the -channel feature. The network topology is described by its adjacency matrix , where when , and otherwise. Node sensitive features are specified by the -channel of , i.e., . Details of all notations used in this work are summarized in Table 7 in Appendix A.

2.2. Fairness in Machine Learning

Group fairness and individual fairness are two commonly encountered fairness notions in real life (Du et al., 2020). Group fairness emphasizes that algorithms should not yield discriminatory outcomes for any specific demographic group (Dong et al., 2022a) while individual fairness requires that similar individuals be treated similarly (Dong et al., 2021). Here we focus on group fairness with a binary sensitive feature, i.e., , but our framework could be generalized to multi-sensitive groups and we leave this as one future direction. Following (Dai and Wang, 2021; Agarwal et al., 2021; Dong et al., 2022a), we employ the difference of statistical parity and equal opportunity between two different sensitive groups, to evaluate the model fairness:


where measures the difference of the independence level of the prediction (true positive rate) on the sensitive feature

between two groups. Since group fairness expects algorithms to yield similar outcomes for different demographic groups, fairer machine learning models seek lower

and .

Figure 1. Initial empirical investigation on sensitive leakage and correlation variation on German dataset. (a)-(b) visualize the relationships between model utility/fairness and the sensitive correlation of each masked feature channel222We respectively mask each feature channel and train a 1-layer MLP/GCN followed by a linear prediction layer. Dataset and experimental details are given in Section 5.1.. Masking channel with less sensitive correlation leads to more biased predictions and sometimes higher model utility. (c)-(d) shows the correlation variation caused by feature propagation on German and Credit datasets. In (c), we can see sensitive correlations of the 2 and 7 feature channel significantly change after propagation while in (d), the correlations do not change so much.

3. Sensitive attribute leakage and correlation variation

In this section, we study the phenomenon where sensitive information leaks to innocuous feature channels after their correlations to the sensitive feature increase during feature propagation in GNNs, which we define as sensitive attribute leakage . We first empirically verify feature channels with higher correlation to the sensitive channel would cause more discrimination in predictions (Zhao et al., 2022). We denote the Pearson correlation coefficient of the -feature channel to the sensitive channel as sensitive correlation and compute it as:



denote the mean and standard deviation of the channel

. Intuitively, higher indicates that the -feature channel encodes more sensitive-related information, which would impose more discrimination in the prediction. To further verify this assumption, we mask each channel and train a 1-layer MLP/GCN followed by a linear layer to make predictions. As suggested by (Agarwal et al., 2021)

, we do not add any activation function in the MLP/GCN to avoid capturing any nonlinearity.

Figure 2(a)-(b) visualize the relationships between the model utility/bias and the sensitive correlation of each masked feature channel. Clearly, we see that the discrimination does still exist even though we mask the sensitive channel (1). Compared with no masking situation, and almost always become lower when we mask other non-sensitive feature channels (2-4), which indicates the leakage of sensitive information to other non-sensitive feature channels. Moreover, we observe the decreasing trend of and when masking channels with higher sensitive correlation since these channels encode more sensitive information and masking them would alleviate more discrimination.

Following the above observation, one natural way to prevent sensitive attribute leakage and alleviate discrimination is to mask the sensitive features as well as its highly-correlated non-sensitive features. However, feature propagation in GNNs could change feature distributions of different channels and consequentially vary feature correlations as shown by Figure 2(c) where we visualize the sensitive correlations of the first 8 feature channels on German after a certain number of propagations. We see that correlations between the sensitive features and other channels change after propagation. For example, some feature channels that are originally irrelevant to the sensitive one, such as the feature channel, become highly-correlated and hence encode more sensitive information.

Encoder Strategy German Credit
MLP S 71.98 82.32 29.26 19.43 74.46 81.64 11.85 9.61
S 69.89 81.37 8.25 4.75 73.49 81.50 11.50 9.20
S 70.54 81.44 6.58 3.24 73.49 81.50 11.50 9.20
GCN S 74.11 82.46 35.17 25.17 73.86 81.92 12.86 10.63
S 73.78 81.65 11.39 9.60 72.92 81.84 12.00 9.70
S 72.75 81.70 8.29 6.91 72.92 81.84 12.00 9.70
GIN S 72.71 82.78 13.56 9.47 74.36 82.28 14.48 12.35
S 71.66 82.50 3.01 1.72 73.44 83.23 14.29 11.79
S 70.77 83.53 1.46 2.67 73.28 83.27 13.96 11.34
  • * S: training using the original feature matrix without any masking.

  • * S/S: training with masking the top- channels based on the rank of /.

Table 1. Evaluating model utility and fairness when using various strategies of feature masking (or no masking).

After observing that feature propagation could vary feature correlation and cause sensitive attribute leakage, we devise two simple but effective masking strategies to highlight the importance of considering correlation variation and sensitive attribute leakage in alleviating discrimination. Specifically, we first compute sensitive correlations of each feature channel according to 1) the original features and 2) the propagated features . Then, we manually mask top- feature channels according to the absolute values of correlation given by and , respectively, and train MLP/GCN/GIN on German/Credit dataset shown in Table 1. Detailed experimental settings are presented in Section 5. From Table 1, we have following insightful observations: (1) Within the same encoder, masking sensitive and its related feature channels (S, S) would alleviate the discrimination while downgrading the model utility compared with no-masking (S). (2) GCN achieves better model utility but causes more bias compared with MLP on German and Credit. This implies graph structures also encode bias and leveraging them could aggravate discrimination in predictions, which is consistent with recent work (Dai and Wang, 2021; Dong et al., 2022a). (3) Most importantly, S achieves lower than S for both MLP and GCN on German because the rank of sensitive correlation changes after feature propagation and masking according to S leads to better fairness, which highlights the importance of considering feature propagation in determining which feature channels are more sensitive-correlated and required to be masked. Applying S achieves the same utility/bias as S on Credit due to less correlation variations shown in Figure 2(d).

To this end, we argue that it is necessary to consider feature propagation in masking feature channels in order to alleviate discrimination. However, the correlation variation heavily depends on the propagation mechanism of GNNs. To tackle this challenge, we formulate our problem as:

Given an attributed network with labels for a subset of nodes , we aim to learn a fair view generator

with the expectation of simultaneously preserving task-related information and discarding sensitive information such that the downstream node classifier

trained on could achieve better trade-off between model utility and fairness.

4. Framework

In this section, we give a detailed description of FairVGNN (shown in Figure 2), which includes the generative adversarial debiasing module and the adaptive weight clamping module. In the first module, we learn a generator that generates different fair views of features to obfuscate the sensitive discriminator such that the encoder could obtain fair node representations for downstream tasks. In the second module, we propose to clamp weights of the encoder based on learned fair feature views, and provide a theoretical justification on its equivalence to minimizing the upper bound of the difference of representations between two different sensitive groups. Next, we introduce the details of each component.

4.1. Generative Adversarial Debiasing

This module includes a fair view generator , a GNN-based encoder , a sensitive discriminator , and a classifier parametrized by , respectively. We assume the view generator to be a learnable latent distribution from which we sample -different masks and generate -corresponding views . The latent distribution would be updated towards generating less-biased views and the stochasticity of each view would enhance the model generalizability. Then each of these -different views are fed to the encoder together with the network topology to learn node representations for downstream classifier . Meanwhile, the learned node representations are used by the sensitive discriminator to predict nodes’ sensitive features. This paves us a way to adopt adversarial learning to obtain the optimal fair view generator where the generated views encode as much task-relevant information while discarding as much bias-relevant information as possible. We begin with introducing the fairness-aware view generator .

Figure 2. An overview of the Fair View Graph Neural Network (FairVGNN), with two main modules: (a) generative adversarial debiasing to learn fair view of features and (b) adaptive weight clamping to clamp weights of sensitive-related channels of the encoder.

4.1.1. Fairness-aware View Generator

As observed in Table 1, discrimination could be traced back to the sensitive features as well as their highly-correlated non-sensitive features. Therefore, we propose to learn a view generator that automatically identifies and masks these features. More specifically, assuming the view generator as a conditional distribution parametrized by , since bias originates from the node features and is further varied by the graph topology

, the conditional distribution of the view generator can be further expressed as a joint distribution of the attribute generator and the topological generator as

. Since our sensitive discriminator is directly trained on the learned node representations from GNN-based encoder as described in Section 4.1.3, we already consider the proximity-induced bias in alleviating discrimination and hence the network topology is assumed to be fixed here, i.e., . We will leave the joint generation of fair feature and topological views as one future work.

Instead of generating from scratch that completely loses critical information for GNN predictions, we generate conditioned on the original node features , i.e., . Following the preliminary experiments, we model the generation process of as identifying and masking sensitive features and their highly-correlated features in . One natural way is to select features according to their correlations to the sensitive features as defined in Eq. (3). However, as shown by Figure 2

(c), feature propagation in GNNs triggers the correlation variation. Thus, instead of masking according to initial correlations that might change after feature propagation, we train a learnable mask for feature selections in a data-driven fashion. Denote our mask as

so that:


then learning the conditional distribution of the feature generator is transformed to learning a sampling distribution of the masker

. We assume the probability of masking each feature channel independently follows a Bernoulli distribution, i.e.,

with each feature channel being masked with the learnable probability . In this way, we can learn which feature channels should be masked to achieve less discrimination through gradient-based techniques. Since the generator aims to obfuscate the discriminator that predicts the sensitive features based on the already-propagated node representations from the encoder , the generated fair feature view would consider the effect of correlation variation by feature propagation rather than blindly follow the order of the sensitive correlations computed by the original features . Generating fair feature view and forwarding it through the encoder and the classifier to make predictions involve sampling masks from the categorical Bernoulli distribution, the whole process of which is non-differentiable due to the discreteness of masks. Therefore, we apply Gumbel-Softmax trick (Jang et al., 2017) to approximate the categorical Bernoulli distribution. Assuming for each channel , we have a learnable sampling score with score keeping while score masking the channel . Then the categorical distribution is softened by333We use instead of thereafter to represent the probability of keeping channel .:


where and is the temperature factor controlling the sharpness of the Gumbel-Softmax distribution. Then, to generate after we sample masks based on probability , we could either directly multiply feature channel by the probability or solely append the gradient of to the sampled hard mask444, both of which are differentiable and can be trained end to end. After we approximate the generator via Gumbel-Softmax, we next model the GNN-based encoder to capture the information of both node features and network topology .

4.1.2. GNN-based Encoder

In order to learn from both the graph topology and node features, we employ layer GNNs as our encoder-backbone to obtain node representations . Different graph convolutions adopt different propagation mechanisms, resulting in different variations on feature correlations. Here we select GCN (Kipf and Welling, 2017), GraphSAGE (Hamilton et al., 2017), and GIN (Xu et al., 2019) as our encoder-backbones. In order to consider the variation induced by the propagation of GNN-based encoders, we apply the discriminator and classifier on top of the obtained node representations from the GNN-based encoders. Since both of the classifier and the discriminator are to make predictions, one towards sensitive groups and the other towards class labels, their model architectures are similar and therefore we introduce them together next.

4.1.3. Classifier and Discriminator

Given node representations obtained from any layer GNN-based encoder , the classifier and the discriminator predict node labels and sensitive attributes as:


where we use two different multilayer perceptrons (MLPs):

for the classifier and the discriminator, and is the sigmoid operation. After introducing the fairness-aware view generator, the GNN-based encoder, the MLP-based classifier and discriminator, we collect them together and adversarially train them with the following objective function.

4.1.4. Adversarial Training

Our goal is to learn fair views from the original graph that encode as much task-relevant information while discarding as much sensitive-relevant information as possible. Therefore, we aim to optimize the whole framework from both the fairness and model utility perspectives. According to statistical parity, to optimize the fairness metric, a fair feature view should guarantee equivalent predictions between sensitive groups:


where is the predicted distribution given the sensitive feature. Assuming and are conditionally independent given  (Kamishima et al., 2011), to solve the global minimum of Eq. (7), we leverage adversarial training and compute the loss of the discriminator and generator as:


where and regularizes the mask to be dense, which avoids masking out sensitive-uncorrelated but task-critical information. is the hyperparamter. Intuitively, Eq. (8) encourages our discriminator to correctly predict the sensitive features of each node under each generated view and Eq. (9) requires our generator to generate fair feature views that enforce the well-trained discriminator to randomly guess the sensitive features. In Theorem 1, we show that the global minimum of Eq. (8)-(9) is equivalent to the global minimum of Eq. (7):

Theorem 1 ().

Given as the representation of a specific node learned by L layer GNN-based encoder and in Eq. (9), the global optimum of Eq. (8)-(9) is equivalent to the one of Eq. (7).


Based on Proposition 1. in (Goodfellow et al., 2014) and Proposition 4.1. in (Dai and Wang, 2021), the optimal discriminator is , which is exactly the probability when discriminator randomly guesses the sensitive features. Then we further substituted it into Eq. (9) and the optimal generator is achieved when . Then we have:

which is obviously the global minimum of Eq. (7). ∎

Note that node representations have already been propagated in GNN-based encoder and therefore, the optimal discriminator could identify sensitive-related features after correlation variation. Besides the adversarial training loss to ensure the fairness of the generated view, the classification loss for training the classifier is used to guarantee the model utility:


4.2. Adaptive Weight Clamping

Although the generator is theoretically guaranteed to achieve its global minimum by applying adversarial training, in practice the generated views may still encode sensitive information and the corresponding classifier may still make discriminatory decisions. This is because of the unstability of the training process of adversarial learning (Goodfellow et al., 2014) and the entanglement with training classifier.

To alleviate the above issue, we propose to adaptively clamp weights of the encoder

based on the learned masking probability distribution from the generator

. After adversarial training, only the sensitive and its highly-correlated features would have higher probability to be masked and therefore, declining their contributions in

by clamping their corresponding weights in the encoder would discourage the encoder from capturing these features and hence alleviate the discrimination. Concretely, within each training epoch after the adversarial training, we compute the probability of keeping features

by sampling masks and calculate their mean . Then assuming the weights of the first layer in the encoder is , we clamp it by:



is a prefix cutting threshold selected by hyperparameter tuning and

takes the sign of . Intuitively, feature channels masked with higher probability (remained with lower probability ) would have lower threshold in weight clamping and hence their contributions to the representations are weakened. Next, we theoretically rationalize this adaptive weight clamping by demonstrating its equivalence to minimizing the upper bound of the difference of representations between two sensitive groups:

Theorem 2 ().

Given a 1-layer GNN encoder with row-normalized adjacency matrix as the PROP and weight matrix

as TRAN and further assume that features of nodes from two sensitive groups in the network independently and identically follow two different Gaussian distributions, i.e.,

, then the difference of representations also follows a Gaussian with the 2-norm of its mean as:


where and denote the sensitive and non-sensitive features, and is the network homophily.


Substituting the row-normalized adjacency matrix , we have , for any pair of nodes coming from two different sensitive groups , we have:


if the network homophily is and further assuming that neighboring nodes strictly obey the network homophily, i.e., among neighboring nodes of the center node , of them come from the same feature distribution as while of them come from the other feature distribution as , then symmetrically we have:


Combining Eq. (14) and Eq. (13), the distribution of their difference would also be a Gaussian , where:


Taking the norm on the mean , splitting channels into sensitive ones and non-sensitive ones , i.e., and expanding based on the input channel, we have:


where represent the weights of the encoder from feature channel

to the hidden neuron

. Since we know that , we substitute the upper bound here into Eq. (17) and finally end up with:

The left side of Eq. (12) is the difference of representations between two sensitive groups and if it is large, i.e., is very large, then the predictions between these two groups would also be very different, which reflects more discrimination in terms of the group fairness. Additionally, Theorem 2 indicates that the upper bound of the group fairness between two sensitive groups depends on the network homophily , the initial feature difference and the masking probability . As the network homophily decreases, more neighboring nodes come from the other sensitive group and aggregating information of these neighborhoods would smooth node representations between different sensitive groups and reduce the bias. To the best of our knowledge, this is the first work relating the fairness with the network homophily. Furthermore, Eq. (12) proves that clamping weights of the encoder upper bounds the group fairness.

4.3. Training Algorithm

Here we present a holistic algorithm of the proposed FairVGNN. In comparison to vanilla adversarial training, additional computational requirements of FairVGNN come from generating different masks. However, since within each training epoch we can pre-compute the masks as Step 4 before adversarial training and the total number of views becomes constant compared with the whole time used for adversarial training as Step 6-14, the time complexity is still linear proportional to the size of the whole graph, i.e., . The total model complexity includes parameters of the feature masker , the discriminator/classifier and the encoder , which boils down to and hence the same as any other -layer GNN backbones.

Input: an attributed graph , Classifier , Encoder , Generator , Discriminator ,
Output: Learned fairness attribute and Predictions
1 while not converged do
2        for  to  do
               , ,   // Section 4.1.1
               ,   // Section 4.1.2555: stopgrad prevents gradients from being back-propagated.
4       for epoch to  do
               ,   // Section 4.1.3
6       for epoch to  do
               ,   // Section 4.1.3
8       for epoch to  do
               ,   // Section 4.1.4
          // Section 4.2
Algorithm 1 The algorithm of FairVGNN

5. Experiments

In this section, we conduct extensive experiments to evaluate the effectiveness of FairVGNN.

5.1. Experimental Settings

5.1.1. Datasets

We validate the proposed approach on three benchmark datasets (Agarwal et al., 2021; Dong et al., 2022a) with their statistics shown in Table 2.


Dataset German Credit Bail


#Nodes 1000 30,000 18,876
#Edges 22,242 1,436,858 321,308
#Features 27 13 18
Sens. Gender Age Race
Label Good/bad Credit Default/no default Payment Bail/no bail


Table 2. Basic dataset statistics.
Encoder Method German Credit Bail Avg. (Rank)
AUC () F1 () ACC () () () AUC () F1 () ACC () () () AUC () F1 () ACC () () ()
GCN Vanilla 74.110.37 82.460.89 73.441.09 35.177.27 25.175.89 73.870.02 81.920.02 73.670.03 12.860.09 10.630.13 87.080.35 79.020.74 84.560.68 7.350.72 4.960.62 9.17
NIFTY 68.782.69 81.400.54 69.921.14 5.735.25 5.084.29 71.960.19 81.720.05 73.450.06 11.680.07 9.390.07 78.202.78 64.763.91 74.192.57 2.441.29 1.721.08 9.69
EDITS 69.412.33 81.550.59 71.600.89 4.054.48 3.894.23 73.010.11 81.810.28 73.510.30 10.901.22 8.751.21 86.442.17 75.583.77 84.492.27 6.640.39 7.511.20 9.89
FairGNN 67.352.13 82.010.26 69.680.30 3.492.15 3.402.15 71.951.43 81.841.19 73.411.24 12.642.11 10.412.03 87.360.90 77.501.69 82.941.67 6.900.17 4.650.14 9.17
FairVGNN 72.412.10 82.140.42 70.160.86 1.711.68 0.880.58 71.340.41 87.080.74 78.040.33 5.025.22 3.604.31 85.680.37 79.110.33 84.730.46 6.530.67 4.951.22 5.67
GIN Vanilla 72.711.44 82.780.50 73.840.54 13.565.23 9.474.49 74.360.21 82.280.64 74.020.73 14.482.44 12.352.86 86.140.25 76.490.57 81.700.67 8.551.61 6.991.51 9.56
NIFTY 67.614.88 80.463.06 69.923.64 5.263.24 5.345.67 70.900.24 84.050.82 75.590.66 7.094.62 6.223.26 82.334.61 70.646.73 74.469.98 5.571.11 3.411.43 8.56
EDITS 69.351.64 82.800.22 72.080.66 0.860.76 1.721.14 72.351.11 82.470.85 74.070.98 14.1114.45 15.4015.76 80.194.62 68.075.30 73.745.12 6.712.35 5.983.66 11.36
FairGNN 72.950.82 83.160.56 72.241.44 6.884.42 2.061.46 68.664.48 79.475.29 70.335.50 4.673.06 3.941.49 86.140.89 73.671.17 77.902.21 6.331.49 4.741.64 7.64
FairVGNN 71.651.90 82.400.14 70.16 0.32 0.430.54 0.340.41 71.360.72 87.440.23 78.180.20 2.852.01 1.721.80 83.221.60 76.362.20 83.861.57 5.670.76 5.771.26 5.44
SAGE Vanilla 75.740.69 81.251.72 72.241.61 24.306.93 15.557.59 74.581.31 83.380.77 75.280.83 15.651.30 13.341.34 90.710.69 80.990.55 86.720.48 2.161.53 0.840.55 7.31
NIFTY 72.052.15 79.201.19 69.601.50 7.747.80 5.172.38 72.890.44 82.601.25 74.391.35 10.651.65 8.101.91 92.040.89 77.816.03 84.115.49 5.740.38 4.071.28 8.06
EDITS 69.765.46 81.041.09 71.681.25 8.427.35 5.692.16 75.040.12 82.410.52 74.130.59 11.346.36 9.385.39 89.072.26 77.833.79 84.422.87 3.743.54 4.463.50 11.36
FairGNN 65.859.49 82.290.32 70.640.74 7.658.07 4.184.86 70.820.74 83.972.00 75.291.62 6.175.57 5.064.46 91.530.38 82.550.98 87.680.73 1.940.82 1.720.70 5.83
FairVGNN 73.840.52 81.910.63 70.000.25 1.361.90 1.221.49 74.050.20 87.840.32 79.940.30 4.941.10 2.390.71 91.561.71 83.581.88 88.411.29 1.140.67 1.691.13 2.92
Table 3. Model utility and bias of node classification. We compare the proposed FairVGNN (i.e., FairV) against state-of-the-art baselines NIFTY, EDITS, and FairGNN (i.e., Fair) when equiped with various GNN backbones (i.e., GCN, GIN, and SAGE). The best and runner-up results are colored in red and blue. represents the larger, the better while represents the opposite.

5.1.2. Baselines

Several state-of-the-art fair node representation learning models are compared with our proposed FairVGNN. We divide them into two categories: (1) Augmentation-based: this type of methods alleviates discrimination via graph augmentation, where sensitive-related information is removed by modifying the graph topology or node features. NIFTY (Agarwal et al., 2021) simultaneously achieves the Counterfactual Fairness and the stability by contrastive learning. EDITS (Dong et al., 2022a) approximates the inputs’ discrimination via Wasserstein distance and directly minimizes it between sensitive and non-sensitive groups by pruning the graph topology and node features. (2) Adversarial-based: The adversarial-based methods enforce the fairness of node representations by alternatively training the encoder to fool the discriminator and the discriminator to predict the sensitive attributes. FairGNN (Dai and Wang, 2021)

deploys an extra sensitive feature estimator to increase the amount of sensitive information

Since different GNN-backbones may cause different levels of sensitive attribute leakage, we consider to equip each of the above three bias-alleviating methods with three GNN-backbones: GCN (Kipf and Welling, 2017), GIN (Xu et al., 2019), GraphSAGE (Hamilton et al., 2017), e.g., GCN-NIFTY represents the GCN encoder with NIFTY.

5.1.3. Setup

Our proposed FairVGNN is implemented using PyTorch-Geometric 

(et al., 2019). For EDITS666, NIFTY777 and FairGNN888, we use the original code from the authors’ GitHub repository. We aim to provide a rigorous and fair comparison between different models on each dataset by tuning hyperparameters for all models individually and detailed hyperparamter configuration of each baseline is in Appendix B.2. Following (Agarwal et al., 2021) and (Dong et al., 2022a), we use 1-layer GCN, GIN convolution and 2-layer GraphSAGE convolution respectively as our encoder , and use 1 linear layer as our classifier and discriminator . The detailed GNN architecture is described in Appendix B.1. We fix the number of hidden unit of the encoder as 16, the dropout rate as 0.5, the number of generated fair feature views during each training epoch . The learning rates and the training epochs of the generator , the discriminator , the classifier and the encoder are searched from and , the prefix cutting threshold in Eq. (11) is searched from , the whole training epochs as , and . We use the default data splitting following (Agarwal et al., 2021; Dong et al., 2022a) and experimental results are averaged over five repeated executions with five different seeds to remove any potential initialization bias.

5.2. Node Classification

5.2.1. Performance comparison

The model utility and fairness of each baseline is shown in Table 3

. We observe that our FairVGNN consistently performs the best compared with other bias-alleviating methods in terms of the average rank for all datasets and across all evaluation metrics, which indicates the superiority of our model in achieving better trade-off between model utility and fairness. Since no fairness regularization is imposed on GNN encoders equipped with vanilla methods, they generally achieve better model utility. However for this reason, sensitive-related information is also completely free to be encoded in the learned node representations and hence causes higher bias. To alleviate such discrimination, all other methods propose different regularizations to constrain sensitive-related information in learned node representations, which also remove some task-related information and hence sacrifice model utility as expected in Table 

3. However, we do observe that our model can yield lower biased predictions with less utility sacrifice, which is mainly ascribed to two reasons: (1) We generate different fair feature views by randomly sampling masks from learned Gumbel-Softmax distribution and make predictions. This can be regarded as a data augmentation technique by adding noise to node features, which decreases the population risk and enhances the model generalibility (Shorten and Khoshgoftaar, 2019) by creating novel mapping from augmented training points to the label space. (2) The weight clamping module clamps weights of encoder based on feature correlations to the sensitive feature channel, which adaptively remove/keep the sensitive/task-relevant information.

Encoder Model Variants German Credit Bail
AUC () F1 () ACC () () () AUC () F1 () ACC () () () AUC () F1 () ACC () () ()
GCN FairV 72.69 1.67 81.86 0.49 69.840.41 0.77 0.39 0.46 0.34 71.340.41 87.080.74 78.040.33 5.025.22 3.604.31 85.680.37 79.110.33 84.730.46 6.530.67 4.951.22
FairV w/o fm 73.63 1.14 82.280.28 70.881.09 5.563.89 4.413.59 72.510.32 86.152.18 77.832.15 6.942.86 4.642.73 86.980.32 78.080.53 84.590.29 7.240.26 5.750.68
FairV w/o wc 72.08 1.83 82.72 0.50 71.04 1.23 3.19 3.51 0.59 1.12 71.800.47 87.270.47 78.470.34 9.054.55 5.943.61 85.930.38 79.220.29 85.380.25 6.61