[2603.02005] Mitigating topology biases in Graph Diffusion via Counterfactual Intervention

Computer Science > Machine Learning arXiv:2603.02005 (cs) [Submitted on 2 Mar 2026] Title:Mitigating topology biases in Graph Diffusion via Counterfactual Intervention Authors:Wendi Wang, Jiaxi Yang, Yongkang Du, Lu Lin View a PDF of the paper titled Mitigating topology biases in Graph Diffusion via Counterfactual Intervention, by Wendi Wang and 3 other authors View PDF HTML (experimental) Abstract:Graph diffusion models have gained significant attention in graph generation tasks, but they often inherit and amplify topology biases from sensitive attributes (e.g. gender, age, region), leading to unfair synthetic graphs. Existing fair graph generation using diffusion models is limited to specific graph-based applications with complete labels or requires simultaneous updates for graph structure and node attributes, making them unsuitable for general usage. To relax these limitations by applying the debiasing method directly on graph topology, we propose Fair Graph Diffusion Model (FairGDiff), a counterfactual-based one-step solution that mitigates topology biases while balancing fairness and utility. In detail, we construct a causal model to capture the relationship between sensitive attributes, biased link formation, and the generated graph structure. By answering the counterfactual question "Would the graph structure change if the sensitive attribute were different?", we estimate an unbiased treatment and incorporate it into the diffusion process. FairGDiff integrates count...