AI RESEARCH
Uncovering Locally Low-dimensional Structure in Networks by Locally Optimal Spectral Embedding
arXiv CS.LG
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ArXi:2603.11965v1 Announce Type: cross 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