Propagation Risk Index (PRI) Framework – Supplementary Materials (Code and Data)

V. 2.0 This repository provides the complete reproducible implementation supporting the revised manuscript “Quantifying Propagation Risk in Distributed Critical Infrastructures: A Unified Framework for AI Failures and GPS Spoofing.” Version 2.0 includes all updated datasets, simulations, and figures used in the resubmitted manuscript, expanding the empirical validation to both healthcare and aviation infrastructures. The Propagation Risk Index (PRI) is a resilience-aware metric that quantifies how erroneous signals propagate across distributed systems by integrating node-level error probability, resilience mechanisms, and criticality weighting. The framework models both endogenous AI failures and exogenous GPS spoofing disruptions, offering a unified formulation applicable to heterogeneous infrastructures. New in Version 2.0: Complete multi-country aviation analysis (Poland, Estonia, Finland, Sweden, Germany, Norway) Updated European aviation datasets (OpenFlights and OurAirports subsets) Synthea-derived realistic hospital IT network (32 nodes, 33 flows) New Python scripts for cross-domain simulations Updated PRI implementation with criticality and resilience extensions Sensitivity analysis generator (±20 percent perturbation) Baseline comparisons vs. degree, betweenness, PageRank, and k-core Reconstructed figures for the resubmitted manuscript (Figures 1, 2, A1) Removal of legacy Lithuania synthetic dataset (not used in v2.0) Unified workflow for healthcare and aviation simulation pipelines CSV outputs for all experiments Reproducibility:All experiments in the manuscript can be fully reproduced using the provided Python scripts. Output values and plots match the results presented in the revised paper, including healthcare simulations, multi-country aviation analysis, hub-dominance profiling, and sensitivity testing. Keywords:Propagation risk, resilience analysis, critical infrastructure, AI failures, GPS spoofing, aviation security, healthcare IT systems, network science, error propagation, SyntheaV 1.0This repository contains the complete implementation and datasets supporting the research paper "Quantifying Propagation Risk in Distributed Critical Infrastructures: A Unified Framework for AI Failures and GPS Spoofing." The Propagation Risk Index (PRI) is a novel metric that quantifies error propagation across distributed infrastructures, integrating node-level error probability, resilience factors, and criticality weighting. This framework addresses both endogenous AI failures and exogenous GPS spoofing threats in critical infrastructures. Contents:- Python implementation of PRI calculation algorithm- Network generation scripts for healthcare and aviation topologies- European aviation network datasets (OpenFlights/OurAirports subsets)- Simulation code for dual-domain validation- Baseline comparison implementations (degree, betweenness, PageRank, k-core)- Data processing scripts for 2025 European GPS spoofing case study- Generated figures and statistical analysis outputs- Documentation and usage examples The code enables full reproduction of all results presented in the paper, including synthetic healthcare networks, European aviation network analysis, and empirical validation against 2025 GPS disruption patterns. All simulations are based on Monte Carlo methods with configurable parameters for error probability, resilience factors, and criticality weights. Keywords: propagation risk, network resilience, critical infrastructure, AI failures, GPS spoofing, network analysis, cybersecurity