Prior-Guided Spatiotemporal GNN for Robust Causal Discovery in Irregular Telecom Alarms

Causal discovery in telecommunication networks is challenging because alarms have irregular timing, uncertain propagation, and incomplete labeling. Existing methods often fail to ensure robustness, accuracy, and interpretability. We propose CausalGNN-Net, which integrates temporal modeling, network topology, and expert priors. A Transformer-based temporal embedding module captures timing with causal masking, a spatiotemporal graph constructor combines topology and co-occurrence with GNN message passing and adaptive edge dropout, a directional graph learner enforces acyclicity, and a prior-guided refiner aligns results with domain knowledge. Training with contrastive loss, sparsity, priors, and calibration improves stability and interpretability. CausalGNN-Net provides a unified and practical solution for causal discovery in telecom alarms.