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Life sciences · Preprint
arXiv · September 10, 2026
Posted before peer review. The findings may change or fail to hold.
This is a preprint describing a machine-learning method to estimate algebraic-connectivity loss in road networks after edge deletion using graph neural networks with spectral priors. The work compares GCN, GraphSAGE, and MPNN models on synthetic and real OpenStreetMap data across six countries, finding that spectral-residual correction improves mean absolute error (MAE) for some failure modes but shows mixed transfer performance. It is not a clinical study and has not undergone peer review.
Computational methods development with simulation and zero-shot transfer learning on graph data. Synthetic road-network graphs and real OpenStreetMap data. Intervention: Graph neural networks (GCN, GraphSAGE, MPNN) with spectral-residual learning to estimate algebraic-connectivity loss after multi-edge deletion. Compared with: First-order Fiedler sensitivity, second-order perturbation, and analytical baselines. 13 OpenStreetMap areas in six countries.
Residual GCN improves spatial-failure MAE by 0.0391 (95% hierarchical interval 0.0151–0.0662) Residual GraphSAGE improves targeted-failure MAE by 0.0257 (95% hierarchical interval 0.0095–0.0446) Second-order perturbation improves first-order MAE by only 0.0028–0.0053
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This is an unrefereed computational methods paper presenting a machine-learning approach to graph connectivity estimation, not a clinical or biomedical study; evidence rating does not apply.
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Rapid evaluation of many simultaneous road-link disruptions requires a practical compromise between exact spectral recomputation and local approximation. We estimate relative algebraic-connectivity loss after multi-edge deletion using graph neural networks (GNNs) that learn a bounded correction to a first-order Fiedler sensitivity. The study considers independent, spatially clustered, and edge-betweenness-targeted failures, with graph-disjoint synthetic splits and zero-shot transfer to 13 OpenStreetMap (OSM) areas in six countries. GCN, GraphSAGE, and edge-aware MPNN backbones are compared with analytical baselines. In expanded OSM tests, residual GCN improves spatial-failure MAE by 0.0391 (95% hierarchical interval 0.0151-0.0662), while residual GraphSAGE improves targeted-failure MAE by 0.0257 (0.0095-0.0446). Second-order perturbation improves first-order MAE by only 0.0028-0.0053. Correction slopes decrease under targeted transfer, indicating residual shrinkage around systematic prior error. Leave-one-country-out OSM-to-OSM transfer is mixed: residual GCN improves targeted-failure MAE by 0.0622 (0.0169-0.1153) but worsens the spatial point estimate. Sparse scaling extends to 20,000 nodes and separates one-time spectral setup from amortized screening cost. These results characterize the spectral residual as a useful but domain-sensitive inductive bias for structural connectivity screening. Code, cached networks, and reproducibility artifacts are archived at doi:10.5281/zenodo.22307723.
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