Uncovering Locally Low-dimensional Structure in Networks by Locally Optimal Spectral Embedding
Standard Adjacency Spectral Embedding (ASE) relies on a global low-rank assumption often incompatible with the sparse, transitive structure of real-world networks, causing local geometric features to be 'smeared'. To address this, we introduce Local Adjacency Spectral Embedding (LASE), which uncovers locally low-dimensional structure via weighted spectral decomposition. Under a latent position model with a kernel feature map, we treat the image of latent positions as a locally low-dimensional set in infinite-dimensional feature space. We establish finite-sample bounds quantifying the trade-off between the statistical cost of localisation and the reduced truncation error achieved by target...
| Authors | Hannah Sansford, Nick Whiteley, Patrick Rubin-Delanchy |
|---|---|
| Primary category | stat.ML |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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