Comprehensive benchmarking of group-fair graph neural networks across five real-world datasets (NBA, Income, Recidivism, Pokec_z, Pokec_n). This project integrates and evaluate models: FairGNN, BIND, NIFTY, MLP, and Vanilla GCN to investigate algorithmic approaches to mitigating group bias in graph-based learning systems