CELL: A Causal Perspective for Fairness-aware Graph Adaptation

This paper investigates fairness-aware graph adaptation, aiming to transfer knowledge from a labeled source graph to an unlabeled target graph while explicitly accounting for fairness. Most prior methods rely on adversarial learning to learn invariant graph representations of sensitive attributes. However, these approaches assume that sensitive attributes of the target domain are available, which often fails in real-world deployments. To address this limitation, we propose \underline{C}ausality-attended Repres\underline{e}ntation Dientang\underline{l}ement with Structural A\underline{l}ignment (CELL) for fairness-aware graph adaptation without requiring target sensitive labels. The key idea of CELL is to build a causal graph that captures the underlying graph-generation mechanism and guides representation disentanglement toward improved fairness. In particular, CELL employs a sensitive encoder and a causal encoder to extract sensitive and causal factors respectively. We encourage disentanglement by minimizing the mutual information between causal and sensitive representations, considering the conditional distribution. To leverage unlabeled target data, we further generate pseudo-labels for both target task labels and sensitive attributes, and use similarity relations to derive unbiased node representations. Finally, to further mitigate domain shift, we build a fairness-aware bipartite graph that provides additional structural supervision for cross-domain alignment. Extensive experiments on benchmark datasets demonstrate that CELL consistently outperforms strong baselines in both predictive performance and fairness.