118 citations · 202 across the 17 of their papers we have counts for
17 papers
Minimax rates for latent position estimation in the generalized random dot product graph
Hao Yan, Keith Levin
Latent space models play an important role in the modeling and analysis of network data. Under these models, each node has an associated latent point in some (typically low-dimensi…
Predicting Responses from Weighted Networks with Node Covariates in an Application to Neuroimaging
Daniel Kessler, Keith Levin, Elizaveta Levina
We consider the setting where many networks are observed on a common node set, and each observation comprises edge weights of a network, covariates observed at each node, and an ov…
Fast Generation of Exchangeable Sequence of Clusters Data
Keith Levin, Brenda Betancourt
Recent advances in Bayesian models for random partitions have led to the formulation and exploration of Exchangeable Sequences of Clusters (ESC) models. Under ESC models, it is the…
Vertex Nomination in Richly Attributed Networks
Keith Levin, Carey E. Priebe, Vince Lyzinski
Vertex nomination is a lightly-supervised network information retrieval task in which vertices of interest in one graph are used to query a second graph to discover vertices of int…
Matrix Means and a Novel High-Dimensional Shrinkage Phenomenon
Asad Lodhia, Keith Levin, Elizaveta Levina
Many statistical settings call for estimating a population parameter, most typically the population mean, based on a sample of matrices. The most natural estimate of the population…
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…
Bootstrapping Networks with Latent Space Structure
Keith Levin, Elizaveta Levina
A core problem in statistical network analysis is to develop network analogues of classical techniques. The problem of bootstrapping network data stands out as especially challengi…
Recovering shared structure from multiple networks with unknown edge distributions
Keith Levin, Asad Lodhia, Elizaveta Levina
In increasingly many settings, data sets consist of multiple samples from a population of networks, with vertices aligned across these networks. For example, brain connectivity net…
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…
On consistent vertex nomination schemes
Vince Lyzinski, Keith Levin, Carey E. Priebe
Given a vertex of interest in a network , the vertex nomination problem seeks to find the corresponding vertex of interest (if it exists) in a second network . A vertex n…
Statistical inference on random dot product graphs: a survey
Avanti Athreya, Donniell E. Fishkind, Keith Levin +7
The random dot product graph (RDPG) is an independent-edge random graph that is analytically tractable and, simultaneously, either encompasses or can successfully approximate a wid…
Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings
Shane Settle, Keith Levin, Herman Kamper +1
Query-by-example search often uses dynamic time warping (DTW) for comparing queries and proposed matching segments. Recent work has shown that comparing speech segments by represen…
A central limit theorem for an omnibus embedding of multiple random graphs and implications for multiscale network inference
Keith Levin, Avanti Athreya, Minh Tang +3
Performing statistical analyses on collections of graphs is of import to many disciplines, but principled, scalable methods for multi-sample graph inference are few. Here we descri…
On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching
Vince Lyzinski, Keith Levin, Donniell E. Fishkind +1
Given a graph in which a few vertices are deemed interesting a priori, the vertex nomination task is to order the remaining vertices into a nomination list such that there is a con…
Laplacian Eigenmaps from Sparse, Noisy Similarity Measurements
Keith Levin, Vince Lyzinski
Manifold learning and dimensionality reduction techniques are ubiquitous in science and engineering, but can be computationally expensive procedures when applied to large data sets…
A Unified Approach for Clustering Problems on Sliding Windows
Vladimir Braverman, Harry Lang, Keith Levin +1
We explore clustering problems in the streaming sliding window model in both general metric spaces and Euclidean space. We present the first polylogarithmic space -approximat…