118 citations · 602 across the 43 of their papers we have counts for
14 papers · 1 filter
Chain-linked multiple matrix integration via embedding alignment
Runbing Zheng, Minh Tang
Motivated by the increasing demand for multi-source data integration in various scientific fields, in this paper we study matrix completion in scenarios where the data exhibits cer…
Independence testing for inhomogeneous random graphs
Yukun Song, Carey E. Priebe, Minh Tang
Testing for independence between graphs is a problem that arises naturally in social network analysis and neuroscience. In this paper, we address independence testing for inhomogen…
Hypothesis Testing for Equality of Latent Positions in Random Graphs
Xinjie Du, Minh Tang
We consider the hypothesis testing problem that two vertices and of a generalized random dot product graph have the same latent positions, possibly up to scaling. Special c…
Two-sample Testing on Latent Distance Graphs With Unknown Link Functions
Yiran Wang, Minh Tang, Soumendra Nath Lahiri
We propose a valid and consistent test for the hypothesis that two latent distance random graphs on the same vertex set have the same generating latent positions, up to some uniden…
Valid Two-Sample Graph Testing via Optimal Transport Procrustes and Multiscale Graph Correlation with Applications in Connectomics
Jaewon Chung, Bijan Varjavand, Jesus Arroyo +5
Testing whether two graphs come from the same distribution is of interest in many real world scenarios, including brain network analysis. Under the random dot product graph model,…
On estimation and inference in latent structure random graphs
Avanti Athreya, Minh Tang, Youngser Park +1
We define a latent structure model (LSM) random graph as a random dot product graph (RDPG) in which the latent position distribution incorporates both probabilistic and geometric c…
Central Limit Theorems for Classical Multidimensional Scaling
Gongkai Li, Minh Tang, Nichlas Charon +1
Classical multidimensional scaling is a widely used method in dimensionality reduction and manifold learning. The method takes in a dissimilarity matrix and outputs a low-dimension…
Asymptotically efficient estimators for stochastic blockmodels: the naive MLE, the rank-constrained MLE, and the spectral
Minh Tang, Joshua Cape, Carey E. Priebe
We establish asymptotic normality results for estimation of the block probability matrix in stochastic blockmodel graphs using spectral embedding when the average degr…
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…
Robust Estimation from Multiple Graphs under Gross Error Contamination
Runze Tang, Minh Tang, Joshua T. Vogelstein +1
Estimation of graph parameters based on a collection of graphs is essential for a wide range of graph inference tasks. In practice, weighted graphs are generally observed with edge…
Consistency of adjacency spectral embedding for the mixed membership stochastic blockmodel
Patrick Rubin-Delanchy, Carey E. Priebe, Minh Tang
The mixed membership stochastic blockmodel is a statistical model for a graph, which extends the stochastic blockmodel by allowing every node to randomly choose a different communi…
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…
Empirical Bayes Estimation for the Stochastic Blockmodel
Shakira Suwan, Dominic S. Lee, Runze Tang +3
Inference for the stochastic blockmodel is currently of burgeoning interest in the statistical community, as well as in various application domains as diverse as social networks, c…
Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown
Donniell E. Fishkind, Daniel L. Sussman, Minh Tang +2
For random graphs distributed according to a stochastic block model, we consider the inferential task of partioning vertices into blocks using spectral techniques. Spectral partion…