118 citations · 602 across the 43 of their papers we have counts for
23 papers · 1 filter
Out-of-Sample Embedding with Proximity Data: Projection versus Restricted Reconstruction
Michael W. Trosset, Kaiyi Tan, Minh Tang +1
The problem of using proximity (similarity or dissimilarity) data for the purpose of "adding a point to a vector diagram" was first studied by J.C. Gower in 1968. Since then, a num…
Adversarial contamination of networks in the setting of vertex nomination: a new trimming method
Sheyda Peyman, Minh Tang, Vince Lyzinski
As graph data becomes more ubiquitous, the need for robust inferential graph algorithms to operate in these complex data domains is crucial. In many cases of interest, inference is…
Classification of high-dimensional data with spiked covariance matrix structure
Yin-Jen Chen, Minh Tang
We study the classification problem for high-dimensional data with observations on features where the covariance matrix exhibits a spiked eigenvalue struct…
Popularity Adjusted Block Models are Generalized Random Dot Product Graphs
John Koo, Minh Tang, Michael W. Trosset
We connect two random graph models, the Popularity Adjusted Block Model (PABM) and the Generalized Random Dot Product Graph (GRDPG), by demonstrating that the PABM is a special cas…
Exact Recovery of Community Structures Using DeepWalk and Node2vec
Yichi Zhang, Minh Tang
Random-walk based network embedding algorithms like DeepWalk and node2vec are widely used to obtain Euclidean representation of the nodes in a network prior to performing downstrea…
Learning 1-Dimensional Submanifolds for Subsequent Inference on Random Dot Product Graphs
Michael W. Trosset, Mingyue Gao, Minh Tang +1
A random dot product graph (RDPG) is a generative model for networks in which vertices correspond to positions in a latent Euclidean space and edge probabilities are determined by…
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…
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Carey E. Priebe, Youngser Park, Joshua T. Vogelstein +6
Clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering - clustering the vertices of a graph based o…
The eigenvalues of stochastic blockmodel graphs
Minh Tang
We derive the limiting distribution for the largest eigenvalues of the adjacency matrix for a stochastic blockmodel graph when the number of vertices tends to infinity. We show tha…
A statistical interpretation of spectral embedding: the generalised random dot product graph
Patrick Rubin-Delanchy, Joshua Cape, Minh Tang +1
Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph. This paper proposes a generalisation of the latent position network mo…
Supervised Dimensionality Reduction for Big Data
Joshua T. Vogelstein, Eric Bridgeford, Minh Tang +4
To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will b…
Semiparametric spectral modeling of the Drosophila connectome
Carey E. Priebe, Youngser Park, Minh Tang +8
We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussia…
Limit theorems for eigenvectors of the normalized Laplacian for random graphs
Minh Tang, Carey E. Priebe
We prove a central limit theorem for the components of the eigenvectors corresponding to the largest eigenvalues of the normalized Laplacian matrix of a finite dimensional rand…
Community Detection and Classification in Hierarchical Stochastic Blockmodels
Vince Lyzinski, Minh Tang, Avanti Athreya +2
We propose a robust, scalable, integrated methodology for community detection and community comparison in graphs. In our procedure, we first embed a graph into an appropriate Eucli…
Perfect Clustering for Stochastic Blockmodel Graphs via Adjacency Spectral Embedding
Vince Lyzinski, Daniel Sussman, Minh Tang +2
Vertex clustering in a stochastic blockmodel graph has wide applicability and has been the subject of extensive research. In thispaper, we provide a short proof that the adjacency…
Out-of-sample Extension for Latent Position Graphs
Minh Tang, Youngser Park, Carey E. Priebe
We consider the problem of vertex classification for graphs constructed from the latent position model. It was shown previously that the approach of embedding the graphs into some…
Generalized Canonical Correlation Analysis for Classification
Cencheng Shen, Ming Sun, Minh Tang +1
For multiple multivariate data sets, we derive conditions under which Generalized Canonical Correlation Analysis (GCCA) improves classification performance of the projected dataset…
Universally consistent vertex classification for latent positions graphs
Minh Tang, Daniel L. Sussman, Carey E. Priebe
In this work we show that, using the eigen-decomposition of the adjacency matrix, we can consistently estimate feature maps for latent position graphs with positive definite link f…
Statistical inference on errorfully observed graphs
Carey E. Priebe, Daniel L. Sussman, Minh Tang +1
Statistical inference on graphs is a burgeoning field in the applied and theoretical statistics communities, as well as throughout the wider world of science, engineering, business…
Generalized Canonical Correlation Analysis for Disparate Data Fusion
Ming Sun, Carey E. Priebe, Minh Tang
Manifold matching works to identify embeddings of multiple disparate data spaces into the same low-dimensional space, where joint inference can be pursued. It is an enabling method…
Universally Consistent Latent Position Estimation and Vertex Classification for Random Dot Product Graphs
Daniel L. Sussman, Minh Tang, Carey E. Priebe
In this work we show that, using the eigen-decomposition of the adjacency matrix, we can consistently estimate latent positions for random dot product graphs provided the latent po…
On latent position inference from doubly stochastic messaging activities
Nam H. Lee, Jordan Yoder, Minh Tang +1
We model messaging activities as a hierarchical doubly stochastic point process with three main levels, and develop an iterative algorithm for inferring actors' relative latent pos…
A consistent adjacency spectral embedding for stochastic blockmodel graphs
Daniel L. Sussman, Minh Tang, Donniell E. Fishkind +1
We present a method to estimate block membership of nodes in a random graph generated by a stochastic blockmodel. We use an embedding procedure motivated by the random dot product…