SI-Fair: Adaptive Stochastic Interpolants for Fair Graph Representation Learning

Fairness in graph neural networks is particularly challenging because sensitive information can be encoded not only in node attributes but also in graph topology. This creates a tension between individual fairness, which encourages similar predictions for structurally similar nodes, and counterfactual fairness, which requires predictions to remain invariant to changes in sensitive attributes. To address this issue, we propose SI-Fair, an adaptive stochastic transport framework that jointly models these two fairness objectives through continuous distribution transformation. SI-Fair first disentangles node representations into unbiased structural information, bias-associated components, sensitive attributes, and auxiliary features, thereby separating task-relevant structural semantics from sensitive information. It then employs stochastic interpolants to construct a continuous transport path from the original biased representation distribution toward a fairness-aware target distribution. A dynamic adjustment mechanism adaptively modulates the stochasticity of the transport process according to the relative optimization progress of the two fairness objectives, enabling a flexible balance between structural consistency and counterfactual invariance. We further establish a theoretical connection between the learned transport process and fairness preservation by deriving an upper bound on the Kullback–Leibler divergence to the target distribution and relating its components to individual and counterfactual fairness objectives. Experiments on multiple real-world graph datasets demonstrate that SI-Fair consistently improves complementary fairness measures while preserving competitive predictive performance. These results indicate that continuous stochastic transport provides an effective framework for reconciling multiple fairness requirements in graph representation learning.