23 citations · 69 across the 6 of their papers we have counts for
6 papers
Asymptotically efficient estimators for stochastic blockmodels: the naive MLE, the rank-constrained MLE, and the spectral
Minh Tang, Joshua Cape, Carey E. Priebe
We establish asymptotic normality results for estimation of the block probability matrix in stochastic blockmodel graphs using spectral embedding when the average degr…
Robust Estimation from Multiple Graphs under Gross Error Contamination
Runze Tang, Minh Tang, Joshua T. Vogelstein +1
Estimation of graph parameters based on a collection of graphs is essential for a wide range of graph inference tasks. In practice, weighted graphs are generally observed with edge…
Consistency of adjacency spectral embedding for the mixed membership stochastic blockmodel
Patrick Rubin-Delanchy, Carey E. Priebe, Minh Tang
The mixed membership stochastic blockmodel is a statistical model for a graph, which extends the stochastic blockmodel by allowing every node to randomly choose a different communi…
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…
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…