Mitigating topology biases in Graph Diffusion via Counterfactual Intervention

Researchers have developed FairGDiff, a new AI model that addresses bias issues in graph diffusion models used for generating synthetic network data. The model uses counterfactual intervention to eliminate topology biases related to sensitive attributes like gender and age while maintaining data utility.

• →FairGDiff introduces a counterfactual-based approach to mitigate topology biases in graph diffusion models.
• →The model addresses fairness issues by eliminating biases related to sensitive attributes like gender, age, and region.
• →Unlike existing methods, FairGDiff works on general graph topology without requiring complete labels or simultaneous updates.
• →The solution integrates counterfactual learning into both forward diffusion and backward denoising processes.
• →Experiments show FairGDiff achieves superior trade-offs between fairness and utility while maintaining scalability.