谱先验何时有助于图学习？道路网络中断下的连通性损失估计
When does a spectral prior help graph learning? Connectivity-loss estimation under road-network disruptions
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中文总结 AI 辅助
本研究提出用图神经网络学习有界修正来估计道路网络多边删除后的连通性损失，实验表明残差GCN和GraphSAGE在空间及目标故障下优于基线，但谱残差作为归纳偏置具有领域敏感性。
中文摘要 AI 辅助
对许多同时发生的道路链路中断进行快速评估，需要在精确谱重计算与局部近似之间取得实际折中。我们使用图神经网络（GNN）估计多边删除后的相对代数连通性损失，该网络学习对一阶Fiedler灵敏度的有界修正。研究考虑了独立、空间聚类和基于边介数目标的故障，采用图不相交的合成划分，并零样本迁移到六个国家的13个OpenStreetMap（OSM）区域。将GCN、GraphSAGE和边感知MPNN骨干网络与分析基线进行比较。在扩展的OSM测试中，残差GCN将空间故障的平均绝对误差（MAE）降低了0.0391（95%分层区间0.0151-0.0662），而残差GraphSAGE将目标故障的MAE降低了0.0257（0.0095-0.0446）。二阶扰动仅将一阶MAE改善了0.0028-0.0053。在目标迁移下修正斜率减小，表明残差在系统性先验误差周围收缩。留一国家出的OSM到OSM迁移结果不一：残差GCN将目标故障MAE改善了0.0622（0.0169-0.1153），但空间点估计变差。稀疏扩展可扩展到20,000个节点，并将一次性谱设置与摊销筛选成本分离。这些结果将谱残差表征为用于结构连通性筛选的有用但领域敏感的归纳偏置。代码、缓存网络和可复现性工件存档于doi：https://doi.org/10.5281/zenodo.22307723。
英文摘要
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.
发表机构
- University of Science（理科大学）
- Viet Nam National University Ho Chi Minh City（越南国立大学胡志明市分校）
机构由 AI 辅助整理，请以论文原文为准。
