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no code implementations • 16 Sep 2023 • Hayden Helm, Carey E. Priebe, Weiwei Yang

Implicit in these efforts is an assumption that the generation properties of a human are different from that of the machine.

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.

no code implementations • 9 May 2023 • Brandon Duderstadt, Hayden S. Helm, Carey E. Priebe

Further, we demonstrate how our methodology can be extended to facilitate population level model comparison.

no code implementations • 4 May 2023 • Aranyak Acharyya, Joshua Agterberg, Michael W. Trosset, Youngser Park, Carey E. Priebe

We assume that the latent position vectors lie on an unknown one-dimensional curve and are coupled with a response covariate via a regression model.

1 code implementation • 3 May 2023 • Cencheng Shen, Jonathan Larson, Ha Trinh, Xihan Qin, Youngser Park, Carey E. Priebe

The analysis of large-scale time-series network data, such as social media and email communications, remains a significant challenge for graph analysis methodology.

1 code implementation • 31 Mar 2023 • Cencheng Shen, Carey E. Priebe, Jonathan Larson, Ha Trinh

In this paper, we introduce a novel approach to multi-graph embedding called graph fusion encoder embedding.

no code implementations • 27 Feb 2023 • Hayden S. Helm, Ashwin De Silva, Joshua T. Vogelstein, Carey E. Priebe, Weiwei Yang

We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of domain adaptation.

1 code implementation • 18 Jan 2023 • Cencheng Shen, Youngser Park, Carey E. Priebe

In this paper we propose a novel and computationally efficient method to simultaneously achieve vertex embedding, community detection, and community size determination.

no code implementations • 26 Oct 2022 • Carey E. Priebe, Ningyuan Huang, Soledad Villar, Cong Mu, Li Chen

We conjecture that for general label noise, mitigation strategies that make use of the noisy data will outperform those that ignore the noisy data.

1 code implementation • 24 Sep 2022 • Ningyuan Huang, Soledad Villar, Carey E. Priebe, Da Zheng, Chengyue Huang, Lin Yang, Vladimir Braverman

Graph Neural Networks (GNNs) are powerful deep learning methods for Non-Euclidean data.

no code implementations • 29 Aug 2022 • Cong Mu, Youngser Park, Carey E. Priebe

We propose a dynamic network sampling scheme to optimize block recovery for stochastic blockmodel (SBM) in the case where it is prohibitively expensive to observe the entire graph.

1 code implementation • 23 Aug 2022 • Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein

In this work, we show a counter-intuitive phenomenon: the generalization error of a task can be a non-monotonic function of the number of OOD samples.

no code implementations • ICCV 2023 • Qingyang Wang, Michael A. Powell, Ali Geisa, Eric Bridgeford, Carey E. Priebe, Joshua T. Vogelstein

Why do deep networks have negative weights?

no code implementations • 28 May 2022 • Li Chen, Ningyuan Huang, Cong Mu, Hayden S. Helm, Kate Lytvynets, Weiwei Yang, Carey E. Priebe

Our hierarchical approach improves upon regular deep neural networks in learning with label noise.

1 code implementation • 17 Mar 2022 • Rajeev Yasarla, Carey E. Priebe, Vishal Patel

Although various weather degradation synthesis methods exist in the literature, the use of synthetically generated weather degraded images often results in sub-optimal performance on the real weather degraded images due to the domain gap between synthetic and real-world images.

no code implementations • 25 Feb 2022 • Guodong Chen, Hayden S. Helm, Kate Lytvynets, Weiwei Yang, Carey E. Priebe

We consider the problem of extracting features from passive, multi-channel electroencephalogram (EEG) devices for downstream inference tasks related to high-level mental states such as stress and cognitive load.

no code implementations • 19 Jan 2022 • Ashwin De Silva, Rahul Ramesh, Lyle Ungar, Marshall Hussain Shuler, Noah J. Cowan, Michael Platt, Chen Li, Leyla Isik, Seung-Eon Roh, Adam Charles, Archana Venkataraman, Brian Caffo, Javier J. How, Justus M Kebschull, John W. Krakauer, Maxim Bichuch, Kaleab Alemayehu Kinfu, Eva Yezerets, Dinesh Jayaraman, Jong M. Shin, Soledad Villar, Ian Phillips, Carey E. Priebe, Thomas Hartung, Michael I. Miller, Jayanta Dey, Ningyuan, Huang, Eric Eaton, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Randal Burns, Onyema Osuagwu, Brett Mensh, Alysson R. Muotri, Julia Brown, Chris White, Weiwei Yang, Andrei A. Rusu, Timothy Verstynen, Konrad P. Kording, Pratik Chaudhari, Joshua T. Vogelstein

We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.

1 code implementation • 9 Nov 2021 • Ali Saad-Eldin, Benjamin D. Pedigo, Carey E. Priebe, Joshua T. Vogelstein

The graph matching problem seeks to find an alignment between the nodes of two graphs that minimizes the number of adjacency disagreements.

no code implementations • 29 Sep 2021 • Ali Geisa, Ronak Mehta, Hayden S. Helm, Jayanta Dey, Eric Eaton, Jeffery Dick, Carey E. Priebe, Joshua T. Vogelstein

This assumption renders these theories inadequate for characterizing 21$^{st}$ century real world data problems, which are typically characterized by evaluation distributions that differ from the training data distributions (referred to as out-of-distribution learning).

2 code implementations • 27 Sep 2021 • Cencheng Shen, Qizhe Wang, Carey E. Priebe

In this paper we propose a lightning fast graph embedding method called one-hot graph encoder embedding.

2 code implementations • 31 Aug 2021 • Haoyin Xu, Kaleab A. Kinfu, Will LeVine, Sambit Panda, Jayanta Dey, Michael Ainsworth, Yu-Chung Peng, Madi Kusmanov, Florian Engert, Christopher M. White, Joshua T. Vogelstein, Carey E. Priebe

Empirically, we compare these two strategies on hundreds of tabular data settings, as well as several vision and auditory settings.

no code implementations • 23 Jun 2021 • Hayden S. Helm, Marah Abdin, Benjamin D. Pedigo, Shweti Mahajan, Vince Lyzinski, Youngser Park, Amitabh Basu, Piali~Choudhury, Christopher M. White, Weiwei Yang, Carey E. Priebe

In modern ranking problems, different and disparate representations of the items to be ranked are often available.

no code implementations • 1 Apr 2021 • Tiona Zuzul, Emily Cox Pahnke, Jonathan Larson, Patrick Bourke, Nicholas Caurvina, Neha Parikh Shah, Fereshteh Amini, Jeffrey Weston, Youngser Park, Joshua Vogelstein, Christopher White, Carey E. Priebe

Workplace communications around the world were drastically altered by Covid-19, related work-from-home orders, and the rise of remote work.

no code implementations • 20 Feb 2021 • Hayden S. Helm, Weiwei Yang, Sujeeth Bharadwaj, Kate Lytvynets, Oriana Riva, Christopher White, Ali Geisa, Carey E. Priebe

In applications where categorical labels follow a natural hierarchy, classification methods that exploit the label structure often outperform those that do not.

no code implementations • 29 Jan 2021 • Al-Fahad M. Al-Qadhi, Carey E. Priebe, Hayden S. Helm, Vince Lyzinski

This paper introduces the subgraph nomination inference task, in which example subgraphs of interest are used to query a network for similarly interesting subgraphs.

1 code implementation • 30 Nov 2020 • Vivek Gopalakrishnan, Jaewon Chung, Eric Bridgeford, Benjamin D. Pedigo, Jesús Arroyo, Lucy Upchurch, G. Allan Johnson, Nian Wang, Youngser Park, Carey E. Priebe, Joshua T. Vogelstein

A connectome is a map of the structural and/or functional connections in the brain.

no code implementations • 12 Nov 2020 • Hayden S. Helm, Ronak D. Mehta, Brandon Duderstadt, Weiwei Yang, Christoper M. White, Ali Geisa, Joshua T. Vogelstein, Carey E. Priebe

Herein we define a measure of similarity between classification distributions that is both principled from the perspective of statistical pattern recognition and useful from the perspective of machine learning practitioners.

no code implementations • 25 Oct 2020 • Carey E. Priebe, Cencheng Shen, Ningyuan Huang, Tianyi Chen

Neural networks have achieved remarkable successes in machine learning tasks.

1 code implementation • 23 Aug 2020 • Guodong Chen, Jesús Arroyo, Avanti Athreya, Joshua Cape, Joshua T. Vogelstein, Youngser Park, Chris White, Jonathan Larson, Weiwei Yang, Carey E. Priebe

We examine two related, complementary inference tasks: the detection of anomalous graphs within a time series, and the detection of temporally anomalous vertices.

Methodology

1 code implementation • 4 Jul 2020 • Cong Mu, Angelo Mele, Lingxin Hao, Joshua Cape, Avanti Athreya, Carey E. Priebe

In network inference applications, it is often desirable to detect community structure, namely to cluster vertices into groups, or blocks, according to some measure of similarity.

2 code implementations • 20 May 2020 • Hayden S. Helm, Amitabh Basu, Avanti Athreya, Youngser Park, Joshua T. Vogelstein, Carey E. Priebe, Michael Winding, Marta Zlatic, Albert Cardona, Patrick Bourke, Jonathan Larson, Marah Abdin, Piali Choudhury, Weiwei Yang, Christopher W. White

Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest.

no code implementations • 29 Apr 2020 • Keith Levin, Carey E. Priebe, Vince Lyzinski

In this paper, we explore, both theoretically and practically, the dual roles of content (i. e., edge and vertex attributes) and context (i. e., network topology) in vertex nomination.

1 code implementation • 27 Apr 2020 • Joshua T. Vogelstein, Jayanta Dey, Hayden S. Helm, Will LeVine, Ronak D. Mehta, Ali Geisa, Haoyin Xu, Gido M. van de Ven, Emily Chang, Chenyu Gao, Weiwei Yang, Bryan Tower, Jonathan Larson, Christopher M. White, Carey E. Priebe

But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on all tasks (including past and future) with any new data.

no code implementations • 15 Apr 2020 • Michael W. Trosset, Mingyue Gao, Minh Tang, Carey E. Priebe

We submit that techniques for manifold learning can be used to learn the unknown submanifold well enough to realize benefit from restricted inference.

no code implementations • 31 Mar 2020 • Joshua Agterberg, Minh Tang, Carey E. Priebe

Two separate and distinct sources of nonidentifiability arise naturally in the context of latent position random graph models, though neither are unique to this setting.

1 code implementation • 5 Feb 2020 • Jesús Arroyo, Carey E. Priebe, Vince Lyzinski

Graph matching consists of aligning the vertices of two unlabeled graphs in order to maximize the shared structure across networks; when the graphs are unipartite, this is commonly formulated as minimizing their edge disagreements.

1 code implementation • 27 Nov 2019 • Anton Alyakin, Yichen Qin, Carey E. Priebe

To the extent that the robustness of the Wilcoxon test (minimum asymptotic relative efficiency (ARE) of the Wilcoxon test vs the t-test is 0. 864) suggests that the Wilcoxon test should be the default test of choice (rather than "use Wilcoxon if there is evidence of non-normality", the default position should be "use Wilcoxon unless there is good reason to believe the normality assumption"), the results in this article suggest that the LqRT is potentially the new default go-to test for practitioners.

Methodology

no code implementations • 20 Oct 2019 • Sambit Panda, Cencheng Shen, Ronan Perry, Jelle Zorn, Antoine Lutz, Carey E. Priebe, Joshua T. Vogelstein

The $k$-sample testing problem tests whether or not $k$ groups of data points are sampled from the same distribution.

no code implementations • 29 Sep 2019 • Keith Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe

In both cases, we prove that when the underlying graph is generated according to a latent space model called the random dot product graph, which includes the popular stochastic block model as a special case, an out-of-sample extension based on a least-squares objective obeys a central limit theorem about the true latent position of the out-of-sample vertex.

no code implementations • 18 Aug 2019 • Angelo Mele, Lingxin Hao, Joshua Cape, Carey E. Priebe

In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure.

no code implementations • 7 Jul 2019 • Disa Mhembere, Da Zheng, Carey E. Priebe, Joshua T. Vogelstein, Randal Burns

Emerging frameworks avoid the network bottleneck of distributed data with Semi-External Memory (SEM) that uses a single multicore node and operates on graphs larger than memory.

Distributed, Parallel, and Cluster Computing Databases

no code implementations • 5 Jul 2019 • Meghana Madhyastha, Percy Li, James Browne, Veronika Strnadova-Neeley, Carey E. Priebe, Randal Burns, Joshua T. Vogelstein

Empirical results on simulated and real data demonstrate that URerF is robust to high-dimensional noise, where as other methods, such as Isomap, UMAP, and FLANN, quickly deteriorate in such settings.

no code implementations • 6 May 2019 • Joshua Agterberg, Youngser Park, Jonathan Larson, Christopher White, Carey E. Priebe, Vince Lyzinski

Given a pair of graphs $G_1$ and $G_2$ and a vertex set of interest in $G_1$, the vertex nomination (VN) problem seeks to find the corresponding vertices of interest in $G_2$ (if they exist) and produce a rank list of the vertices in $G_2$, with the corresponding vertices of interest in $G_2$ concentrating, ideally, at the top of the rank list.

1 code implementation • 5 Apr 2019 • Congyuan Yang, Carey E. Priebe, Youngser Park, David J. Marchette

The second contribution is a simultaneous model selection framework.

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 • 23 Aug 2018 • Carey E. Priebe, Youngser Park, Joshua T. Vogelstein, John M. Conroy, Vince Lyzinski, Minh Tang, Avanti Athreya, Joshua Cape, Eric Bridgeford

Clustering is concerned with coherently grouping observations without any explicit concept of true groupings.

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 • ICML 2018 • Keith Levin, Farbod Roosta-Khorasani, Michael W. Mahoney, Carey E. Priebe

Many popular dimensionality reduction procedures have out-of-sample extensions, which allow a practitioner to apply a learned embedding to observations not seen in the initial training sample.

no code implementations • 15 Nov 2017 • Vince Lyzinski, Keith Levin, Carey E. Priebe

Given a vertex of interest in a network $G_1$, the vertex nomination problem seeks to find the corresponding vertex of interest (if it exists) in a second network $G_2$.

1 code implementation • 26 Oct 2017 • Cencheng Shen, Carey E. Priebe, Joshua T. Vogelstein

Understanding and developing a correlation measure that can detect general dependencies is not only imperative to statistics and machine learning, but also crucial to general scientific discovery in the big data age.

no code implementations • 16 Sep 2017 • Patrick Rubin-Delanchy, Joshua Cape, Minh Tang, Carey E. Priebe

Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph.

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.

1 code implementation • 9 May 2017 • Carey E. Priebe, Youngser Park, Minh Tang, Avanti Athreya, Vince Lyzinski, Joshua T. Vogelstein, Yichen Qin, Ben Cocanougher, Katharina Eichler, Marta Zlatic, Albert Cardona

We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome.

no code implementations • 1 May 2017 • Heather G. Patsolic, Youngser Park, Vince Lyzinski, Carey E. Priebe

Consider two networks on overlapping, non-identical vertex sets.

2 code implementations • 10 Mar 2017 • Shangsi Wang, Jesús Arroyo, Joshua T. Vogelstein, Carey E. Priebe

Feature extraction and dimension reduction for networks is critical in a wide variety of domains.

1 code implementation • 2 Jan 2017 • P-A. G. Maugis, Carey E. Priebe, S. C. Olhede, P. J. Wolfe

Our results yield joint confidence regions for subgraph counts, and therefore methods for testing whether the observations in a network sample are drawn from: a specified distribution, a specified model, or from the same model as another network sample.

Methodology Social and Information Networks 62G05, 05C80, 62G10

4 code implementations • 16 Sep 2016 • Joshua T. Vogelstein, Eric Bridgeford, Qing Wang, Carey E. Priebe, Mauro Maggioni, Cencheng Shen

Understanding the relationships between different properties of data, such as whether a connectome or genome has information about disease status, is becoming increasingly important in modern biological datasets.

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 • 28 Jul 2016 • Minh Tang, Carey E. Priebe

As a corollary, we show that for stochastic blockmodel graphs, the rows of the spectral embedding of the normalized Laplacian converge to multivariate normals and furthermore the mean and the covariance matrix of each row are functions of the associated vertex's block membership.

no code implementations • 5 Jul 2016 • Vince Lyzinski, Keith Levin, Donniell E. Fishkind, Carey E. Priebe

Given a graph in which a few vertices are deemed interesting a priori, the vertex nomination task is to order the remaining vertices into a nomination list such that there is a concentration of interesting vertices at the top of the list.

1 code implementation • 28 Jun 2016 • Disa Mhembere, Da Zheng, Carey E. Priebe, Joshua T. Vogelstein, Randal Burns

The \textit{k-means NUMA Optimized Routine} (\textsf{knor}) library has (i) in-memory (\textsf{knori}), (ii) distributed memory (\textsf{knord}), and (iii) semi-external memory (\textsf{knors}) modules that radically improve the performance of k-means for varying memory and hardware budgets.

Distributed, Parallel, and Cluster Computing

2 code implementations • 21 Apr 2016 • Da Zheng, Disa Mhembere, Joshua T. Vogelstein, Carey E. Priebe, Randal Burns

R is one of the most popular programming languages for statistics and machine learning, but the R framework is relatively slow and unable to scale to large datasets.

Distributed, Parallel, and Cluster Computing

2 code implementations • 9 Feb 2016 • Da Zheng, Disa Mhembere, Vince Lyzinski, Joshua Vogelstein, Carey E. Priebe, Randal Burns

In contrast, we scale sparse matrix multiplication beyond memory capacity by implementing sparse matrix dense matrix multiplication (SpMM) in a semi-external memory (SEM) fashion; i. e., we keep the sparse matrix on commodity SSDs and dense matrices in memory.

Distributed, Parallel, and Cluster Computing

no code implementations • 1 Feb 2016 • Jordan Yoder, Carey E. Priebe

Traditionally, practitioners initialize the {\tt k-means} algorithm with centers chosen uniformly at random.

2 code implementations • 10 Jun 2015 • Tyler M. Tomita, James Browne, Cencheng Shen, Jaewon Chung, Jesse L. Patsolic, Benjamin Falk, Jason Yim, Carey E. Priebe, Randal Burns, Mauro Maggioni, Joshua T. Vogelstein

Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on axis-aligned splits, such as robustness to many noise dimensions, interpretability, or computational efficiency.

no code implementations • 7 Mar 2015 • Vince Lyzinski, Minh Tang, Avanti Athreya, Youngser Park, Carey E. Priebe

We propose a robust, scalable, integrated methodology for community detection and community comparison in graphs.

no code implementations • 11 Feb 2015 • Vince Lyzinski, Youngser Park, Carey E. Priebe, Michael W. Trosset

The Joint Optimization of Fidelity and Commensurability (JOFC) manifold matching methodology embeds an omnibus dissimilarity matrix consisting of multiple dissimilarities on the same set of objects.

no code implementations • 4 Feb 2015 • Cencheng Shen, Li Chen, Yuexiao Dong, Carey E. Priebe

The results are demonstrated via simulations and real data experiments, where the new algorithm achieves comparable numerical performance and significantly faster.

1 code implementation • 12 Dec 2014 • Cencheng Shen, Joshua T. Vogelstein, Carey E. Priebe

Then the shortest-path distance within each modality is calculated from the joint neighborhood graph, followed by embedding into and matching in a common low-dimensional Euclidean space.

no code implementations • 25 Nov 2014 • William Gray Roncal, Dean M. Kleissas, Joshua T. Vogelstein, Priya Manavalan, Kunal Lillaney, Michael Pekala, Randal Burns, R. Jacob Vogelstein, Carey E. Priebe, Mark A. Chevillet, Gregory D. Hager

Finally, we deploy a reference end-to-end version of the pipeline on a large, publicly available data set.

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.

no code implementations • 13 May 2014 • Vince Lyzinski, Donniell Fishkind, Marcelo Fiori, Joshua T. Vogelstein, Carey E. Priebe, Guillermo Sapiro

Indeed, experimental results illuminate and corroborate these theoretical findings, demonstrating that excellent results are achieved in both benchmark and real data problems by amalgamating the two approaches.

no code implementations • 29 Dec 2013 • Nam H. Lee, Runze Tang, Carey E. Priebe, Michael Rosen

We consider a problem of clustering a sequence of multinomial observations by way of a model selection criterion.

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 • 21 May 2013 • Minh Tang, Youngser Park, Carey E. Priebe

We show that, under the latent position graph model and for sufficiently large $n$, the mapping of the out-of-sample vertices is close to its true latent position.

no code implementations • 30 Apr 2013 • Cencheng Shen, Ming Sun, Minh Tang, Carey E. Priebe

For multiple multivariate data sets, we derive conditions under which Generalized Canonical Correlation Analysis (GCCA) improves classification performance of the projected datasets, compared to standard Canonical Correlation Analysis (CCA) using only two data sets.

no code implementations • 9 Jan 2013 • Donniell E. Fishkind, Cencheng Shen, Youngser Park, Carey E. Priebe

Suppose that two large, multi-dimensional data sets are each noisy measurements of the same underlying random process, and principle components analysis is performed separately on the data sets to reduce their dimensionality.

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.

no code implementations • 3 Sep 2012 • Donniell E. Fishkind, Sancar Adali, Heather G. Patsolic, Lingyao Meng, Digvijay Singh, Vince Lyzinski, Carey E. Priebe

Given two graphs, the graph matching problem is to align the two vertex sets so as to minimize the number of adjacency disagreements between the two graphs.

no code implementations • 26 May 2012 • Nam H. Lee, Jordan Yoder, Minh Tang, Carey E. Priebe

Each of the message-exchanging actors is modeled as a process in a latent space.

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