activity
20072022
most citedSemiparametric spectral modeling of the Drosophila connectome

23 citations · 99 across the 25 of their papers we have counts for

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Showing 2018Show all

11 papers · 1 filter

stat.ML2018

Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks

Jesús Arroyo, Daniel L. Sussman, Carey E. Priebe +1

Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes…

math.ST2018

On spectral embedding performance and elucidating network structure in stochastic block model graphs

Joshua Cape, Minh Tang, Carey E. Priebe

Statistical inference on graphs often proceeds via spectral methods involving low-dimensional embeddings of matrix-valued graph representations, such as the graph Laplacian or adja…

math.CO2018

Alignment Strength and Correlation for Graphs

Donniell E. Fishkind, Lingyao Meng, Ao Sun +2

When two graphs have a correlated Bernoulli distribution, we prove that the alignment strength of their natural bijection strongly converges to a novel measure of graph correlation…

stat.ML2018

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…

stat.ME2018

Bayesian Estimation of Sparse Spiked Covariance Matrices in High Dimensions

Fangzheng Xie, Yanxun Xu, Carey E. Priebe +1

We propose a Bayesian methodology for estimating spiked covariance matrices with jointly sparse structure in high dimensions. The spiked covariance matrix is reparametrized in term…

stat.ME2018

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…