Data and code from: PathVGAE: A path-based variational graph autoencoder framework for ranking centrality in road networks

Natural hazards including wildfires, hurricanes, and floods change network topology, which in turn, affect the vulnerability of road network components (e.g., intersections and road segments). Therefore, a dynamic assessment of the road network vulnerability is essential during disruptions to obtain up-to-date information on at-risk components. However, dynamically assessing vulnerability requires repeatedly recalculating centrality measures, which can be computationally expensive and time-consuming. To address this, we propose a machine learning architecture called PathVGAE that leverages the embedding structure of a Variational Graph Auto-Encoder (VGAE) with a path sampling encoder to learn latent representations that capture key topological features for centrality predictions. Our model can accurately identify high importance roads in seconds by leveraging only the static structure of the network. The experimental results demonstrate that PathVGAE outperforms baseline models in accurately ranking high importance roads, making it a valuable tool for vulnerability analysis of complex transit networks during disruptions.