Learning fair representations via rebalancing graph structure

Abstract

Graph Neural Network (GNN) models have been extensively researched and utilised for extracting valuable insights from graph data. The performance of fairness algorithms based on GNNs depends on the neighbourhood aggregation mechanism during the update process. However, this mechanism may result in the disregard of sensitive attributes pertaining to nodes with low degrees, as well as the disproportionate influence of sensitive attributes associated with high-degree nodes on their neighbouring nodes. To address these limitations, we propose a novel algorithm called Structural Rebalancing Graph Neural Network (SRGNN). SRGNN aims to consider the impact of both low-degree and high-degree nodes in the GNN model for learning fair representations in decision-making. SRGNN first proposes a fair structural rebalancing algorithm to ensure equal status among nodes by reducing the influence of high-degree nodes and enhancing the influence of low-degree nodes. Next, SRGNN utilises adversarial learning to learn fair representations, based on gradient normalisation to ensure that each node’s representation is separated from sensitive attribute information. We conducted extensive experiments on three real-world datasets to evaluate the performance of SRGNN. The results showed that SRGNN outperformed all existing models in 2 out of 2 fairness metrics.