Replication Package: Predicting Urban Economic Resilience via Graph Convolutional Networks and Functional Urban Networks (N=93 China)

Description: This repository contains the replication package for the manuscript titled "[Insert Your Paper Title]". This study investigates the transmission of economic resilience across functional urban networks using a Graph Convolutional Network (GCN) framework. Key Contributions: Sample Expansion: Addresses small-sample bias by pooling 93 prefecture-level cities across four major Chinese urban agglomerations (JJJ, YRD, PRD, CD-CQ) into a 93×93 Block-Diagonal adjacency matrix. Causal-Logic Alignment: Implements a strict temporal cutoff, utilizing pre-pandemic features (2019) to predict pandemic-impact resilience (2020-2023), mitigating information leakage. Methodological Rigor: Compares GCN performance against established machine learning baselines (Random Forest, XGBoost) and topological null models (Random Graph) using a leave-one-city-out cross-validation (LOOCV) framework. Resilience Regimes: Demonstrates the superiority of GCN in classification tasks (High/Medium/Low resilience), revealing how economic flow networks capture resilience transmission better than geographical proximity. Contents: data/: Processed feature matrices (X_pooled.npy), OD flow matrices (A_pooled.npy), and GDP-based Resilience Index (y_pooled.npy). scripts/: Standardized Python scripts for feature generation, experiment execution, and figure plotting. results/: CSV files containing the aggregated classification performance metrics. Usage: Please ensure Python 3.10+ is installed with the dependencies listed in requirements.txt. Run python run_pooled_classification.py to replicate the main classification results.