NeuBM: Mitigating Model Bias in Graph Neural Networks through Neutral Input Calibration

TL;DR

NeuBM introduces a neutral input calibration method for GNNs that dynamically estimates and corrects model bias, significantly improving fairness and accuracy in imbalanced class scenarios with minimal computational cost.

Contribution

NeuBM presents a novel bias mitigation technique using a neutral graph to recalibrate GNN predictions, enhancing fairness especially for minority classes.

Findings

NeuBM improves balanced accuracy and recall for minority classes.

The method maintains overall performance with minimal computational overhead.

NeuBM is especially effective in severe class imbalance and limited data scenarios.

Abstract

Graph Neural Networks (GNNs) have shown remarkable performance across various domains, yet they often struggle with model bias, particularly in the presence of class imbalance. This bias can lead to suboptimal performance and unfair predictions, especially for underrepresented classes. We introduce NeuBM (Neutral Bias Mitigation), a novel approach to mitigate model bias in GNNs through neutral input calibration. NeuBM leverages a dynamically updated neutral graph to estimate and correct the inherent biases of the model. By subtracting the logits obtained from the neutral graph from those of the input graph, NeuBM effectively recalibrates the model's predictions, reducing bias across different classes. Our method integrates seamlessly into existing GNN architectures and training procedures, requiring minimal computational overhead. Extensive experiments on multiple benchmark datasets demonstrate that NeuBM significantly improves the balanced accuracy and recall of minority classes, while maintaining strong overall performance. The effectiveness of NeuBM is particularly pronounced in scenarios with severe class imbalance and limited labeled data, where traditional methods often struggle. We provide theoretical insights into how NeuBM achieves bias mitigation, relating it to the concept of representation balancing. Our analysis reveals that NeuBM not only adjusts the final predictions but also influences the learning of balanced feature representations throughout the network.