118 citations · 1.3k across the 129 of their papers we have counts for
8 papers · 2 filters
A model selection approach for clustering a multinomial sequence with non-negative factorization
Nam H. Lee, Runze Tang, Carey E. Priebe +1
We consider a problem of clustering a sequence of multinomial observations by way of a model selection criterion. We propose a form of a penalty term for the model selection proced…
Vertex nomination schemes for membership prediction
D. E. Fishkind, V. Lyzinski, H. Pao +2
Suppose that a graph is realized from a stochastic block model where one of the blocks is of interest, but many or all of the vertices' block labels are unobserved. The task is to…
Robust Vertex Classification
Li Chen, Cencheng Shen, Joshua Vogelstein +1
For random graphs distributed according to stochastic blockmodels, a special case of latent position graphs, adjacency spectral embedding followed by appropriate vertex classificat…
Spectral Clustering for Divide-and-Conquer Graph Matching
Vince Lyzinski, Daniel L. Sussman, Donniell E. Fishkind +5
We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs. Our algorithm combines spectral graph embedding with e…
Perfect Clustering for Stochastic Blockmodel Graphs via Adjacency Spectral Embedding
Vince Lyzinski, Daniel Sussman, Minh Tang +2
Vertex clustering in a stochastic blockmodel graph has wide applicability and has been the subject of extensive research. In thispaper, we provide a short proof that the adjacency…
Out-of-sample Extension for Latent Position Graphs
Minh Tang, Youngser Park, Carey E. Priebe
We consider the problem of vertex classification for graphs constructed from the latent position model. It was shown previously that the approach of embedding the graphs into some…