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- D E Fishkind, V Lyzinski, H Pao, L Chen, C E Priebe
- 2015

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 order the vertices with unobserved block labels into a " nomination list " such that, with high probability, vertices from the interesting block are concentrated near the list's… (More)

- Joshua T. Vogelstein, John M. Conroy, +6 authors Carey E. Priebe
- 2011

Graph matching (GM)—the process of finding an optimal permutation of the vertices of one graph to minimize adjacency disagreements with the vertices of another—is rapidly becoming an increasingly important computational problem, arising in fields ranging from machine vision to neuroscience. Because GM is N P-hard, exact algorithms are unsuitable for today's… (More)

- Vince Lyzinski, Donniell E. Fishkind, Carey E. Priebe
- Journal of Machine Learning Research
- 2014

Graph matching is an important problem in machine learning and pattern recognition. Herein, we present theoretical and practical results on the consistency of graph matching for estimating a latent alignment function between the vertex sets of two graphs, as well as subsequent algorithmic implications when the latent alignment is partially observed. In the… (More)

- Vince Lyzinski, Donniell E. Fishkind, Marcelo Fiori, Joshua T. Vogelstein, Carey E. Priebe, Guillermo Sapiro
- IEEE Transactions on Pattern Analysis and Machine…
- 2016

Graph matching-aligning a pair of graphs to minimize their edge disagreements-has received wide-spread attention from both theoretical and applied communities over the past several decades, including combinatorics, computer vision, and connectomics. Its attention can be partially attributed to its computational difficulty. Although many heuristics have… (More)

- Vince Lyzinski, Daniel L. Sussman, +5 authors Carey E. Priebe
- Parallel Computing
- 2015

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 existing state-of-the-art seeded graph matching procedures. We justify our approach by proving that modestly correlated, large stochastic block model random graphs are correctly… (More)

- Joshua T. Vogelstein, John M. Conroy, +6 authors Carey E. Priebe
- PloS one
- 2015

Quadratic assignment problems arise in a wide variety of domains, spanning operations research, graph theory, computer vision, and neuroscience, to name a few. The graph matching problem is a special case of the quadratic assignment problem, and graph matching is increasingly important as graph-valued data is becoming more prominent. With the aim of… (More)

- Vince Lyzinski, Gregory Sell, Aren Jansen
- INTERSPEECH
- 2015

Unsupervised term discovery (UTD) is the task of automatically identifying the repeated words and phrases in a collection of speech audio without relying on any language-specific resources. While the solution space for the task is far from fully explored, the dominant approach to date decomposes the discovery problem into two steps, where (i) segmental… (More)

Vertex clustering in a stochastic blockmodel graph has wide applicability and has been the subject of extensive research. In this paper, we provide a short proof that the adjacency spectral embedding can be used to obtain perfect clustering for the stochastic blockmodel and the degree-corrected stochastic blockmodel. We also show an analogous result for the… (More)

- Vince Lyzinski, Minh Tang, Avanti Athreya, Youngser Park, Carey E. Priebe
- IEEE Transactions on Network Science and…
- 2017

In disciplines as diverse as social network analysis and neuroscience, many large graphs are believed to be composed of loosely connected smaller graph primitives, whose structure is more amenable to analysis We propose a robust, scalable, integrated methodology for <italic>community detection</italic> and <italic>community comparison</italic> in graphs. In… (More)

- Li Chen, Joshua T Vogelstein, Vince Lyzinski, Carey E Priebe
- Worm
- 2016

We investigate joint graph inference for the chemical and electrical connectomes of the Caenorhabditis elegans roundworm. The C. elegans connectomes consist of [Formula: see text] non-isolated neurons with known functional attributes, and there are two types of synaptic connectomes, resulting in a pair of graphs. We formulate our joint graph inference from… (More)