14 citations · 14 across the 1 of their papers we have counts for
7 papers
Spectral inference for large Stochastic Blockmodels with nodal covariates
Angelo Mele, Lingxin Hao, Joshua Cape +1
In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure. To this end, we develop spectral estim…
Inference for multiple heterogeneous networks with a common invariant subspace
Jesús Arroyo, Avanti Athreya, Joshua Cape +3
The development of models for multiple heterogeneous network data is of critical importance both in statistical network theory and across multiple application domains. Although sin…
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…
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…
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…
Signal-plus-noise matrix models: eigenvector deviations and fluctuations
Joshua Cape, Minh Tang, Carey E. Priebe
Estimating eigenvectors and low-dimensional subspaces is of central importance for numerous problems in statistics, computer science, and applied mathematics. This paper characteri…