Root Laplacian Eigenmaps with their application in spectral embedding
arXiv:2302.02731v11 citationsh-index: 1
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This addresses spectral clustering and graph analysis, but appears incremental as it adapts an existing geometric concept to graphs.
The paper introduces the root Laplacian operator for graphs, an analog of a concept from Riemannian geometry, and applies it to spectral embedding, with potential uses in geometric deep learning and graph signal processing.
The root laplacian operator or the square root of Laplacian which can be obtained in complete Riemannian manifolds in the Gromov sense has an analog in graph theory as a square root of graph-Laplacian. Some potential applications have been shown in geometric deep learning (spectral clustering) and graph signal processing.