# Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings

@article{Levin2019LimitTF, title={Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings}, author={Keith Levin and Fred Roosta and Minh Tang and Michael W. Mahoney and Carey E. Priebe}, journal={ArXiv}, year={2019}, volume={abs/1910.00423} }

Graph embeddings, a class of dimensionality reduction techniques designed for relational data, have proven useful in exploring and modeling network structure. Most dimensionality reduction methods allow out-of-sample extensions, by which an embedding can be applied to observations not present in the training set. Applied to graphs, the out-of-sample extension problem concerns how to compute the embedding of a vertex that is added to the graph after an embedding has already been computed. In…

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