The augmentation graph Laplacian converges pointwise and spectrally to a weighted Laplace-Beltrami operator, supporting a bounded-complexity neural approximation of optimal spectral contrastive embeddings.
Error estimates for spectral convergence of the graph laplacian on random geometric graphs toward the laplace–beltrami operator
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Consistency of augmentation graph and network approximability in contrastive learning
The augmentation graph Laplacian converges pointwise and spectrally to a weighted Laplace-Beltrami operator, supporting a bounded-complexity neural approximation of optimal spectral contrastive embeddings.