118 citations · 602 across the 43 of their papers we have counts for
5 papers · 2 filters
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…
Statistical inference on random dot product graphs: a survey
Avanti Athreya, Donniell E. Fishkind, Keith Levin +7
The random dot product graph (RDPG) is an independent-edge random graph that is analytically tractable and, simultaneously, either encompasses or can successfully approximate a wid…
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…
A central limit theorem for an omnibus embedding of multiple random graphs and implications for multiscale network inference
Keith Levin, Avanti Athreya, Minh Tang +3
Performing statistical analyses on collections of graphs is of import to many disciplines, but principled, scalable methods for multi-sample graph inference are few. Here we descri…