Publications

Exact Recovery of Community Structures Using DeepWalk and Node2vec

Random-walk based network embedding algorithms like node2vec and DeepWalk are widely used to obtain Euclidean representation of the …
Yichi Zhang, Minh Tang
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Hypothesis testing for equality of latent positions in random graphs

We consider the hypothesis testing problem that two vertices i and j of a generalized random dot product graph have the same latent …
Xinjie Du, Minh Tang
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Numerical tolerance for spectral decompositions of random matrices

Given a network and a subset of interesting vertices whose identities are only partially known, the vertex nomination problem seeks to …
Avanti Athreya, Michael Kane, Brian Lewis, Vince Lyzinski, Zachary Lubberts, Youngser Park, Carey E. Priebe, Minh Tang
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Popularity adjusted block models are generalized random dot product graphs

We connect two random graph models, the Popularity Adjusted Block Model (PABM) and the Generalized Random Dot Product Graph (GRDPG), by …
John Koo, Minh Tang, Michael Trosset
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Limit results for distributed estimation of invariant subspaces in multiple networks inference and PCA

We study the problem of estimating the left and right singular subspaces for a collection of heterogeneous random graphs with a shared …
Runbing Zheng, Minh Tang
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Perturbation analysis of randomized SVD and its applications to high-dimensional statistics

Randomized singular value decomposition (RSVD) is a class of computationally efficient algorithms for computing the truncated SVD of …
Yichi Zhang, Minh Tang
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A statistical interpretation of spectral embedding: the generalised random dot product graph

A generalisation of a latent position network model known as the random dot product graph model is considered. The resulting model may …
Patrick Rubin-Delanchy, Joshua Cape, Minh Tang, Carey E. Priebe
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Vertex nomination between graphs via spectral embedding and quadratic programming

Given a network and a subset of interesting vertices whose identities are only partially known, the vertex nomination problem seeks to …
Runbing Zheng, Vince Lyznsiki, Carey E. Priebe, Minh Tang
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Adversarial contamination of networks in the setting of vertex nomination: a new trimming method

As graph data becomes more ubiquitous, the need for robust inferential graph algorithms to operate in these complex data domains is …
Sheyda Peyman, Minh Tang, Vince Lyzinski
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Valid two-sample graph testing via optimal transport Procrustes and multiscale graph correlation with applications in connectomics

Testing whether two graphs come from the same distribution is of interest in many real world scenarios, including brain network …
Jaewon Chung, Bijan Varjavand, Jesus Arroyo, Anton Alyakin, Joshua Agterberg, Minh Tang, Joshua T. Vogelstein, Carey E. Priebe
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Asymptotically efficient estimators for stochastic blockmodels: the naive MLE, the rank-constrained MLE, and the spectral

We establish asymptotic normality results for estimation of the block probability matrix $\mathbf{B}$ in stochastic blockmodel graphs …
Minh Tang, Joshua Cape, Carey E. Priebe
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Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings

Performing statistical inference on collections of graphs is of import to many disciplines. Graph embedding, in which the vertices of a …
Keith Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe
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Classification of high-dimensional data with spiked covariance matrix structure

We study the classification problem for high-dimensional data with n observations on p features where the pxp covariance matrix Sigma …
Yin-Jen Chen, Minh Tang
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Eigenvalues of stochastic blockmodel graphs and random graphs with low-rank edge probabilities matrices

We derive the limiting distribution for the largest eigenvalues of the adjacency matrix for a stochastic blockmodel graph when the …
Avanti Athreya, Joshua Cape, Minh Tang
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Supervised dimensionality reduction for big data

To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with …
Joshua T. Vogelstein, Eric W. Bridgeford, Minh Tang, Da Zheng, Christopher Douville, Randall Burns, Mauro Maggioni
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On estimation and inference in latent structure random graphs

We define a latent structure model (LSM) random graph as a random dot product graph (RDPG) in which the latent position distribution …
Avanti Athreya, Minh Tang, Youngser Park, Carey E. Priebe
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Nonparametric Two-Sample Hypothesis Testing for Random Graphs with Negative and Repeated Eigenvalues

We propose a nonparametric two-sample test statistic for low-rank, conditionally independent edge random graphs whose edge probability …
Joshua Agteberg, Minh Tang, Carey E. Priebe
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Two-sample testing on latent distance graphs with unknown link functions

We propose a valid and consistent test for the hypothesis that two latent distance random graphs on the same vertex set have the same …
Yiran Wang, Minh Tang, Soumendra N. Lahiri
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A central limit theorem for classical multidimensional scaling

Classical multidimensional scaling (CMDS) is a widely used method in manifold learning. It takes in a dissimilarity matrix and outputs …
Gongkai Li, Minh Tang, Nicholas Charon, Carey E. Priebe
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Learning 1-dimensional submanifolds for subsequent inference on random dot product graphs

A generalisation of a latent position network model known as the random dot product graph model is considered. The resulting model may …
Michael W. Trosset, Mingyue Gao, Minh Tang, Carey E. Priebe
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On two distinct sources of non-identifiability in latent position random graph models

A generalisation of a latent position network model known as the random dot product graph model is considered. The resulting model may …
Joshua Agteberg, Minh Tang, Carey E. Priebe
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On spectral embedding performance and elucidating network structure

Statistical inference on graphs often proceeds via spectral methods involving low-dimensional embeddings of matrix-valued graph …
Joshua Cape, Minh Tang, Carey E. Priebe
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Signal-plus-noise matrix models: eigenvector deviations and fluctuations

Estimating eigenvectors and low-dimensional subspaces is of central importance for numerous problems in statistics, computer science …
Joshua Cape, Minh Tang, Carey E. Priebe
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The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics

The singular value matrix decomposition plays a ubiquitous role throughout statistics and related fields. Myriad applications including …
Joshua Cape, Minh Tang, Carey E. Priebe
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On a 'two truths' phenomenon in spectral graph clustering

Clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering …
Carey E. Priebe, Youngser Park, Joshua T. Vogelstein, John M. Conroy, Vince Lyzinski, Minh Tang, Avanti Athreya, Joshua Cape, Eric Bridgeford
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Limit theorems for eigenvectors of the normalized Laplacian for random graphs

We prove a central limit theorem for the components of the eigenvectors corresponding to the $d$ largest eigenvalues of the normalized …
Minh Tang, Carey E. Priebe
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Statistical inference on random dot product graphs: a survey

The random dot product graph (RDPG) is an independent-edge random graph that is analytically tractable and, simultaneously, either …
Avanti Athreya, Donniell E. Fishkind, Keith Levin, Vince Lyzinski, Youngser Park, Yichen Qin, Daniel L. Sussman, Minh Tang, Joshua T. Vogelstein, Carey E. Priebe
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A central limit theorem for an omnibus embedding of random dot product graphs

Performing statistical inference on collections of graphs is of import to many disciplines. Graph embedding, in which the vertices of a …
Keith Levin, Avanti Athreya, Minh Tang, Vince Lyzinski, Carey E. Priebe
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The Kato-Temple inequality and eigenvalue concentration with applications to graph inference

We present an adaptation of the Kato-Temple inequality for bounding perturbations of eigenvalues with applications to statistical …
Joshua Cape, Minh Tang, Carey E. Priebe
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A nonparametric two-sample hypothesis testing problem for random dot product graphs

We consider the problem of testing whether two finite-dimensional random dot product graphs have generating latent positions that are …
Minh Tang, Avanti Athreya, Daniel L. Sussman, Vince Lyzinski, Carey E. Priebe
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Robust estimation from multiple graphs under gross error contamination

Estimation of graph parameters based on a collection of graphs is essential for a wide range of graph inference tasks. In practice, …
Runze Tang, Minh Tang, Joshua T. Vogelstein, Carey E. Priebe
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A semiparametric two-sample hypothesis testing problem for random dot product graphs

Two-sample hypothesis testing for random graphs arises naturally in neuroscience, social networks, and machine learning. In this paper, …
Minh Tang, Avanti Athreya, Daniel L. Sussman, Vince Lyzinski, Youngser Park, Carey E. Priebe
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Community detection and classification in hierarchical stochastic blockmodels

We propose a robust, scalable, integrated methodology for community detection and community comparison in graphs. In our procedure, we …
Vince Lyzinski, Minh Tang, Avanti Athreya, Youngser Park, Carey E. Priebe
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Empirical Bayes estimation for the stochastic blockmodels

Inference for the stochastic blockmodel is currently of burgeoning interest in the statistical community, as well as in various …
Shakira Suwan, Dominic S. Lee, Runze Tang, Daniel L. Sussman, Minh Tang, Carey E. Priebe
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A limit theorem for scaled eigenvectors of random dot product graphs

We prove a central limit theorem for the components of the largest eigenvectors of the adjacency matrix of a finite-dimensional random …
Avanti Athreya, Carey E. Priebe, Minh Tang, Vince Lyzinski, David J. Marchette, Daniel L. Sussman
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Statistical inference on errorfully observed graphs

Statistical inference on graphs is a burgeoning field in the applied and theoretical statistics communities, as well as throughout the …
Carey E. Priebe, Daniel L. Sussman, Minh Tang, Joshua T. Vogelstein
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Generalized canonical correlation analysis for classification

For multiple multivariate datasets, we derive conditions under which Generalized Canon- ical Correlation Analysis improves …
Cencheng Shen, Ming Sun, Minh Tang, Carey E. Priebe
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Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding

Vertex clustering in a stochastic blockmodel graph has wide applicability and has been the subject of extensive research. In thispaper, …
Vince Lyzinski, Daniel L. Sussman, Minh Tang, Avanti Athreya, Carey E. Priebe
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Locality statistics for anomaly detection in time series of graphs

The ability to detect change-points in a dynamic network or a time series of graphs is an increasingly important task in many …
Heng Wang, Minh Tang, Youngser Park, Carey E. Priebe
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Consistent latent position estimation and vertex classification for random dot product graphs

In this work, we show that using the eigen-decomposition of the adjacency matrix, we can consistently estimate latent positions for …
Daniel L. Sussman, Minh Tang, Carey E. Priebe
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On latent position inference from doubly stochastic messaging activities

We model messaging activities as a hierarchical doubly stochastic point process with three main levels and develop an iterative …
Nam H. Lee, Jordan Yoder, Minh Tang, Carey E. Priebe
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Universally consistent vertex classification for latent position graphs

In this work we show that, using the eigen-decomposition of the adjacency matrix, we can consistently estimate feature maps for latent …
Minh Tang, Daniel L. Sussman, Carey E. Priebe
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Attribute fusion in a latent process model for time series of graphs

Hypothesis testing on time series of attributed graphs has applications in diverse areas, e.g., social network analysis (wherein …
Minh Tang, Nam H. Lee, Youngser Park, Carey E. Priebe
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Generalized canonical correlation analysis for disparate data fusion

Manifold matching works to identify embeddings of multiple disparate data spaces into the same low-dimensional space, where joint …
Ming Sun, Minh Tang, Carey E. Priebe
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Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown

For random graphs distributed according to a stochastic block model, we consider the inferential task of partitioning vertices into …
Donniell E. Fishkind, Daniel L. Sussman, Minh Tang, Joshua T. Vogelstein, Carey E. Priebe
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A consistent adjacency spectral embedding for stochastic blockmodel graphs

We present a method to estimate block membership of nodes in a random graph generated by a stochastic blockmodel. We use an embedding …
Daniel L. Sussman, Minh Tang, Donniell E. Fishkind, Carey E. Priebe
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