Graph Neural Networks for Efficient Bayesian Network Inference in Safety-Critical Applications

Traditional Bayesian inference methods face critical limitations for real-time safety applications: exponential computational costs, frequent failures, and symmetric error treatment despite asymmetric consequences. This paper presents a Graph Neural Network approach that achieves R²=0.938 with 1.44 ms inference time, providing 8,611 × average speedup over traditional methods while maintaining 100 % reliability. Through asymmetric loss functions that penalize underprediction more heavily in low-probability regions, we achieve mean absolute error of 0.033 in the critical range (p < 0.3) where rare but catastrophic events occur. Evaluation on synthetic and real-world networks demonstrates that Graph Attention Networks eliminate inference failure as a failure mode while enabling real-time continuous risk assessment in autonomous systems, aerospace, and industrial automation.

Keywords: Bayesian Networks, Graph Neural Networks, Safety-Critical Systems, Real-Time Inference, Probabilistic Reasoning.