FairTCD: Dual-Teacher Temporal Contrastive Distillation for Twofold Fair Dynamic Graph Embedding

Abstract

Fair dynamic graph embedding is crucial for real-world systems, such as recommendation and social networks. Prior studies impose a single-axis fairness formulation, treating attribute and structural bias as separable artifacts. This overlooks their coupling relationship, under which debiasing along one axis can induce cross-axis amplification. This coupling further introduces opposing gradient constraints under joint optimization, leading to optimization conflicts. Furthermore, the evolution of dynamic graphs causes shifts in bias distribution, leading to unstable optimization and exacerbating these conflicts. To address these issues, we propose FairTCD, a Fair Dual-Teacher Temporal Contrastive Distillation framework. FairTCD employs two adversarial fairness teachers to decouple attribute and structural fairness representations. To reconcile dynamic conflicts between two fairness objectives, we introduce a temporal contrastive distillation to induce consistency between attribute and structural fairness representations across time while retaining temporal semantics. A unified student model distills complementary knowledge from both teachers to achieve twofold fairness. Experiments on three real-world benchmarks demonstrate that FairTCD preserves performance while improving twofold fairness metrics by at least 4.53%.