23 citations · 70 across the 8 of their papers we have counts for
6 papers · 1 filter
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings
Keith Levin, Fred Roosta, Minh Tang +2
Graph embeddings, a class of dimensionality reduction techniques designed for relational data, have proven useful in exploring and modeling network structure. Most dimensionality r…
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…
The eigenvalues of stochastic blockmodel graphs
Minh Tang
We derive the limiting distribution for the largest eigenvalues of the adjacency matrix for a stochastic blockmodel graph when the number of vertices tends to infinity. We show tha…
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…