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


Aug 20, 2022
Sheyda Peyman, Minh Tang, Vince Lyzinski

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Perturbation Analysis of Randomized SVD and its Applications to High-dimensional Statistics


Mar 19, 2022
Yichi Zhang, Minh Tang

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Classification of high-dimensional data with spiked covariance matrix structure


Oct 05, 2021
Yin-Jen Chen, Minh Tang

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* 40 pages, 2 figures 

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Popularity Adjusted Block Models are Generalized Random Dot Product Graphs


Sep 09, 2021
John Koo, Minh Tang, Michael W. Trosset

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* 33 pages, 7 figures 

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Hypothesis Testing for Equality of Latent Positions in Random Graphs


May 23, 2021
Xinjie Du, Minh Tang

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* 67 pages, 3 figures 

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Consistency of random-walk based network embedding algorithms


Jan 18, 2021
Yichi Zhang, Minh Tang

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Learning 1-Dimensional Submanifolds for Subsequent Inference on Random Dot Product Graphs


Apr 17, 2020
Michael W. Trosset, Mingyue Gao, Minh Tang, Carey E. Priebe

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* 20 pages 

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On Two Distinct Sources of Nonidentifiability in Latent Position Random Graph Models


Mar 31, 2020
Joshua Agterberg, Minh Tang, Carey E. Priebe

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


Sep 29, 2019
Keith Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe

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* Portions of this work originally appeared in ICML2018 as "Out-of-sample extension of graph adjacency spectral embedding" (accompanying technical report available at arXiv:1802.06307). This work extends the results of that earlier paper to a second graph embedding technique called the Laplacian spectral embedding and presents additional experiments 

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