Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinement

In three linesMulti-Scale Hypergraph Laplacians (MSHL): two-stage framework for imputing incomplete spatiotemporal sensor network data. Discovers higher-order structure via multi-scale hypergraphs, then refines with hypergraph-conditioned residual network. Theoretical guarantees and evaluation on real traffic networks with structured outages.

Summary generated by Claude — human-verified