1 citations · 2 across the 4 of their papers we have counts for
3 papers · 1 filter
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings
Keith Levin, Fred Roosta, Minh Tang +2
Graph embeddings, a class of dimensionality reduction techniques designed for relational data, have proven useful in exploring and modeling network structure. Most dimensionality r…
Out-of-sample extension of graph adjacency spectral embedding
Keith Levin, Farbod Roosta-Khorasani, Michael W. Mahoney +1
Many popular dimensionality reduction procedures have out-of-sample extensions, which allow a practitioner to apply a learned embedding to observations not seen in the initial trai…
Vertex nomination: The canonical sampling and the extended spectral nomination schemes
Jordan Yoder, Li Chen, Henry Pao +5
Suppose that one particular block in a stochastic block model is of interest, but block labels are only observed for a few of the vertices in the network. Utilizing a graph realize…