BioMetaEvo-GNN: Bayesian Fourier Message Passing with Immune Memory for Robust Community Detection

Community detection in real-world graphs is challenged by noisy edges, ambiguous node features, perturbation-induced assignment drift, and unstable community boundaries. Existing GNN-based methods typically optimize clustering accuracy or local robustness, but rarely unify uncertainty modeling, memory preservation, and adaptive defense. We propose BioMetaEvo-GNN, which integrates Bayesian Fourier learning, immune-memory defense, and UCB-based meta-governance. Bayesian Fourier learning identifies spectral instability and drift-prone boundaries, immune memory preserves reliable structural evidence and suppresses unstable transitions, and meta-governance adaptively selects strategies using uncertainty, stability, and historical rewards. Experiments on real-world and synthetic graphs show consistent gains in clustering accuracy, perturbation robustness, uncertainty calibration, assignment stability, and cross-dataset generalization over representative GNN and robust graph learning baselines. These results establish community detection as a joint problem of representation learning, uncertainty estimation, and adaptive robustness.