MaGNet-BN: Markov-Guided Bayesian Neural Networks for Calibrated Long-Horizon Sequence Forecasting and Community Tracking

Forecasting over dynamic graph environments necessitates modeling both long-term temporal dependencies and evolving structural patterns. We propose MaGNet-BN, a modular framework that simultaneously performs probabilistic forecasting and dynamic community detection on temporal graphs. MaGNet-BN integrates Bayesian node embeddings for uncertainty modeling, prototype-guided Louvain clustering for community discovery, Markov-based transition modeling to preserve temporal continuity, and reinforcement-based refinement to improve structural boundary accuracy. Evaluated on real-world datasets in pedestrian mobility, energy consumption, and retail demand, our model achieves on average 11.48% lower MSE, 6.62% lower NLL, and 10.82% higher Modularity (Q) compared with the best-performing baselines, with peak improvements reaching 12.0% in MSE, 7.9% in NLL, and 16.0% in Q on individual datasets. It also improves uncertainty calibration (PICP) and temporal community coherence (tARI). Ablation studies highlight the complementary strengths of each component. Overall, MaGNet-BN delivers a structure-aware and uncertainty-calibrated forecasting system that models both temporal evolution and dynamic community formation, with a modular design enabling interpretable predictions and scalable applications across smart cities, energy systems, and personalized services.