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

This 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 examplesThe 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