no code implementations • 25 Aug 2023 • Zhirui Li, Ben Johnson, Daniel L. Sussman, Carey E. Priebe, Vince Lyzinski

We present a novel approach for finding multiple noisily embedded template graphs in a very large background graph.

1 code implementation • 5 May 2020 • Benjamin Draves, Daniel L. Sussman

Joint spectral embeddings facilitate analysis of multiple network data by simultaneously mapping vertices in each network to points in Euclidean space where statistical inference is then performed.

Statistics Theory Statistics Theory 62H12, 62H15, 05C80

no code implementations • 26 Dec 2018 • Jesús Arroyo, Daniel L. Sussman, Carey E. Priebe, Vince Lyzinski

Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes the total number of induced edge disagreements.

no code implementations • 6 Mar 2018 • Daniel L. Sussman, Youngser Park, Carey E. Priebe, Vince Lyzinski

To illustrate the possibilities and challenges of such problems, we use an algorithm that can exploit a partially known correspondence and show via varied simulations and applications to {\it Drosophila} and human connectomes that this approach can achieve good performance.

no code implementations • 16 Sep 2017 • Avanti Athreya, Donniell E. Fishkind, Keith Levin, Vince Lyzinski, Youngser Park, Yichen Qin, Daniel L. Sussman, Minh Tang, Joshua T. Vogelstein, Carey E. Priebe

In this survey paper, we describe a comprehensive paradigm for statistical inference on random dot product graphs, a paradigm centered on spectral embeddings of adjacency and Laplacian matrices.

no code implementations • 6 Sep 2016 • Runze Tang, Michael Ketcha, Alexandra Badea, Evan D. Calabrese, Daniel S. Margulies, Joshua T. Vogelstein, Carey E. Priebe, Daniel L. Sussman

In statistical connectomics, the quantitative study of brain networks, estimating the mean of a population of graphs based on a sample is a core problem.

no code implementations • 25 Jun 2015 • Daniel L. Sussman, Alexander Volfovsky, Edoardo M. Airoldi

The recent explosion in the amount and dimensionality of data has exacerbated the need of trading off computational and statistical efficiency carefully, so that inference is both tractable and meaningful.

no code implementations • 23 May 2014 • Shakira Suwan, Dominic S. Lee, Runze Tang, Daniel L. Sussman, Minh Tang, Carey E. Priebe

Inference for the stochastic blockmodel is currently of burgeoning interest in the statistical community, as well as in various application domains as diverse as social networks, citation networks, brain connectivity networks (connectomics), etc.

1 code implementation • 4 Oct 2013 • Vince Lyzinski, Daniel L. Sussman, Donniell E. Fishkind, Henry Pao, Li Chen, Joshua T. Vogelstein, Youngser Park, Carey E. Priebe

We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs.

no code implementations • 31 May 2013 • Avanti Athreya, Vince Lyzinski, David J. Marchette, Carey E. Priebe, Daniel L. Sussman, Minh Tang

We prove a central limit theorem for the components of the largest eigenvectors of the adjacency matrix of a finite-dimensional random dot product graph whose true latent positions are unknown.

no code implementations • 5 Dec 2012 • Minh Tang, Daniel L. Sussman, Carey E. Priebe

In this work we show that, using the eigen-decomposition of the adjacency matrix, we can consistently estimate feature maps for latent position graphs with positive definite link function $\kappa$, provided that the latent positions are i. i. d.

no code implementations • 15 Nov 2012 • Carey E. Priebe, Daniel L. Sussman, Minh Tang, Joshua T. Vogelstein

Thus we errorfully observe $G$ when we observe the graph $\widetilde{G} = (V,\widetilde{E})$ as the edges in $\widetilde{E}$ arise from the classifications of the "edge-features", and are expected to be errorful.

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