Minh Tang

Eigenvector fluctuations and limit results for random graphs with infinite rank kernels

(ArXiv Preprint No. .2501.15725).

Chain-linked Multiple Matrix Integration via Embedding Alignment

(ArXiv Preprint No. 2412.02791).

Regression for matrix-valued data via Kronecker products factorization

(ArXiv Preprint No. .2404.19220).

A Theoretical Analysis of DeepWalk and Node2vec for Exact Recovery of Community Structures in Stochastic Blockmodels

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 46(2), 1065–1078.

Hypothesis testing for equality of latent positions in random graphs

BERNOULLI, 29(4), 3221–3254.

Independence testing for inhomogeneous random graphs

(ArXiv Preprint No. 2304.09132).

Motif-Based Exploratory Data Analysis for State-Backed Platform Manipulation on Twitter

Proceedings of the International AAAI Conference on Web and Social Media, 17, 315–326.

A statistical interpretation of spectral embedding: The generalised random dot product graph

Rubin-Delanchy, P., Cape, J., Tang, M., & Priebe, C. E. (2022, June 3). JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, Vol. 6.

Adversarial contamination of networks in the setting of vertex nomination: a new trimming method

(ArXiv Preprint No. 2208.09710).

Asymptotically efficient estimators for stochastic blockmodels: The naive MLE, the rank-constrained MLE, and the spectral estimator

BERNOULLI, 28(2), 1049–1073.

Limit results for distributed estimation of invariant subspaces in multiple networks inference and PCA

(ArXiv Preprint No. 2206.04306).

Numerical Tolerance for Spectral Decompositions of Random Matrices and Applications to Network Inference

Athreya, A., Lubberts, Z., Priebe, C. E., Park, Y., Tang, M., Lyzinski, V., … Lewis, B. W. (2022, July 18). JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, Vol. 7.

Perturbation analysis of randomized svd and its applications to high-dimensional statistics

[ArXiv preprint].

Popularity Adjusted Block Models are Generalized Random Dot Product Graphs

Koo, J., Tang, M., & Trosset, M. W. (2022, June 25). JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, Vol. 6.

Vertex Nomination Between Graphs via Spectral Embedding and Quadratic Programming

Zheng, R., Lyzinski, V., Priebe, C. E., & Tang, M. (2022, May 13). JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, Vol. 5.

Classification of high-dimensional data with spiked covariance matrix structure

https://arxiv.org/abs/2110.01950

Eigenvalues of Stochastic Blockmodel Graphs and Random Graphs with Low-Rank Edge Probability Matrices

Athreya, A., Cape, J., & Tang, M. (2021, November 3). SANKHYA-SERIES A-MATHEMATICAL STATISTICS AND PROBABILITY, Vol. 11.

Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings

Journal of Machine Learning Research. https://www.jmlr.org/papers/v22/19-852.html

Supervised dimensionality reduction for big data

Nature Communications, 12(1).

Valid two-sample graph testing via optimal transport Procrustes and multiscale graph correlation with applications in connectomics

STAT, 11(1).

Central limit theorems for classical multidimensional scaling

ELECTRONIC JOURNAL OF STATISTICS, 14(1), 2362–2394.

Motif-Based Exploratory Analysis for State-Sponsored Influence on Twitter.

Poster.

On Estimation and Inference in Latent Structure Random Graphs

STATISTICAL SCIENCE, 36(1), 68–88.

Two sample testing for latent distance graphs with unknown link functions

https://arxiv.org/abs/2008.01038

On a two-truths phenomenon in spectral graph clustering

Proceedings of the National Academy of Sciences of the United States of America, 116(13), 5995–6000.

On spectral embedding performance and elucidating network structure in stochastic blockmodel graphs

NETWORK SCIENCE, 7(3), 269–291.

The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics

Annals of Statistics, 47(5), 2405–2439.

Limit theorems for eigenvectors of the normalized Laplacian for random graphs

Annals of Statistics, 46(5), 2360–2415.

Signal-plus-noise matrix models: Eigenvector deviations and fluctuations

Biometrika, 106(1), 243–250.

Statistical inference on random dot product graphs: A survey

Journal of Machine Learning Research, 18, 1–92. http://www.scopus.com/inward/record.url?eid=2-s2.0-85050718266&partnerID=MN8TOARS

A central limit theorem for an omnibus embedding of multiple random dot product graphs

IEEE International Conference on Data Mining Workshops, ICDMW, 2017-November, 964–967.

A nonparametric two-sample hypothesis testing problem for random graphs

Bernoulli, 23(3), 1599–1630.

The kato-temple inequality and eigenvalue concentration with applications to graph inference

Electronic Journal of Statistics, 11(2), 3954–3978.

A Semiparametric Two-Sample Hypothesis Testing Problem for Random Graphs

Journal of Computational and Graphical Statistics, 26(2), 344–354.

A limit theorem for scaled eigenvectors of random dot product graphs

Sankhya: The Indian Journal of Statistics, 78A, 1–18. http://www.scopus.com/inward/record.url?eid=2-s2.0-84973351119&partnerID=MN8TOARS

Community Detection and Classification in Hierarchical Stochastic Blockmodels

IEEE Transactions on Network Science and Engineering, 4(1), 13–26.

Empirical Bayes estimation for the stochastic blockmodel

Electronic Journal of Statistics, 10(1), 761–782.

Path-cost bounds for parameterized centralized variants of A∗ for static and certain environments

International Journal on Artificial Intelligence Tools, 25(4).

Generalized canonical correlation analysis for classification

Journal of Multivariate Analysis, 130, 310–322.

Locality statistics for anomaly detection in time series of graphs

IEEE Transactions on Signal Processing, 62(3), 703–717.

Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding

Electronic Journal of Statistics, 8(2), 2905–2922.

Statistical Inference on Errorfully Observed Graphs

Journal of Computational and Graphical Statistics, 24(4), 930–953.

Two-sample hypothesis testing for random dot product graphs

JSM 2014. Presented at the JSM 2014.

Attribute Fusion in a Latent Process Model for Time Series of Graphs

IEEE Transactions on Signal Processing, 61(7), 1721–1732.

Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown

SIAM Journal on Matrix Analysis and Applications, 34(1), 23–39.

Consistent latent position estimation and vertex classification for random dot product graphs

IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(1), 48–57.

Inference in time series of graphs using locality statistics

2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings, 471–474.

Metric space structures for computational anatomy

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 123–130).

On Latent Position Inference from Doubly Stochastic Messaging Activities

Multiscale Modeling & Simulation, 11(3), 683–718.

Universally consistent vertex classification for latent positions graphs

The Annals of Statistics.

A comparison of graph embedding methods for vertex nomination

Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012, 1, 398–403.

A consistent adjacency spectral embedding for stochastic blockmodel graphs

Journal of the American Statistical Association, 107(499), 1119–1128.

Generalized canonical correlation analysis for disparate data fusion

Pattern Recognition Letters, 34(2), 194–200.

Attribute fusion in a latent process model for time series of graphs

IEEE Workshop on Statistical Signal Processing Proceedings, 513–516.

Generating Functions and the Solutions of Full History Recurrence Equations

Electronic Notes in Discrete Mathematics, 29(SPEC. ISS.), 445–449.

Rational generating functions and the solution of linear inhomogeneous recurrence equations

WSEAS Transactions on Mathematics, 5(11), 1191–1196. http://www.scopus.com/inward/record.url?eid=2-s2.0-34547685671&partnerID=MN8TOARS

State-space planning with variants of A*

International Journal on Artificial Intelligence Tools, 15(3), 433–464.

Variants of a∗ for planning

In Frontiers in Artificial Intelligence and Applications (Vol. 110, pp. 1095–1096). http://www.scopus.com/inward/record.url?eid=2-s2.0-85017393789&partnerID=MN8TOARS

Search Control Techniques for Planning

Proceedings of the International Conference on Tools with Artificial Intelligence, 168–175. http://www.scopus.com/inward/record.url?eid=2-s2.0-0344235438&partnerID=MN8TOARS