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no code implementations • 20 Aug 2022 • Sheyda Peyman, Minh Tang, Vince Lyzinski

Here, a common suite of methods relies on spectral graph embeddings, which have been shown to provide both good algorithmic performance and flexible settings in which regularization techniques can be implemented to help mitigate the effect of an adversary.

no code implementations • 18 Aug 2022 • Ayushi Saxena, Vince Lyzinski

Many two-sample network hypothesis testing methodologies operate under the implicit assumption that the vertex correspondence across networks is a priori known.

no code implementations • 6 May 2022 • Zhirui Li, Jesus Arroyo, Konstantinos Pantazis, Vince Lyzinski

Given a collection of vertex-aligned networks and an additional label-shuffled network, we propose procedures for leveraging the signal in the vertex-aligned collection to recover the labels of the shuffled network.

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 • 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.

no code implementations • 1 Aug 2020 • Konstantinos Pantazis, Avanti Athreya, Jesús Arroyo, William N. Frost, Evan S. Hill, Vince Lyzinski

We describe how this omnibus embedding can itself induce correlation, leading us to distinguish between inherent correlation -- the correlation that arises naturally in multisample network data -- and induced correlation, which is an artifice of the joint embedding methodology.

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 • 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.

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.

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 • 14 Feb 2018 • Jordan Yoder, Li Chen, Henry Pao, Eric Bridgeford, Keith Levin, Donniell Fishkind, Carey Priebe, Vince Lyzinski

There are vertex nomination schemes in the literature, including the optimally precise canonical nomination scheme~$\mathcal{L}^C$ and the consistent spectral partitioning nomination scheme~$\mathcal{L}^P$.

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 $G_2$.

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.

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.

no code implementations • 8 May 2016 • Vince Lyzinski

While many multiple graph inference methodologies operate under the implicit assumption that an explicit vertex correspondence is known across the vertex sets of the graphs, in practice these correspondences may only be partially or errorfully known.

no code implementations • 12 Mar 2016 • Keith Levin, Vince Lyzinski

In particular, we consider Laplacian eigenmaps embeddings based on a kernel matrix, and explore how the embeddings behave when this kernel matrix is corrupted by occlusion and noise.

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 • 18 Aug 2015 • Aren Jansen, Gregory Sell, Vince Lyzinski

Several popular graph embedding techniques for representation learning and dimensionality reduction rely on performing computationally expensive eigendecompositions to derive a nonlinear transformation of the input data space.

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 • 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 • 16 Jan 2014 • Heather Patsolic, Sancar Adali, Joshua T. Vogelstein, Youngser Park, Carey E. Friebe, Gongkai Li, Vince Lyzinski

We present a novel approximate graph matching algorithm that incorporates seeded data into the graph matching paradigm.

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 • 2 Oct 2013 • Vince Lyzinski, Daniel Sussman, Minh Tang, Avanti Athreya, Carey Priebe

Vertex clustering in a stochastic blockmodel graph has wide applicability and has been the subject of extensive research.

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 • 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.

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