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.