Search Results for author: Vince Lyzinski

Found 30 papers, 4 papers with code

Adversarial contamination of networks in the setting of vertex nomination: a new trimming method

no code implementations20 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.

Information Retrieval Retrieval

Lost in the Shuffle: Testing Power in the Presence of Errorful Network Vertex Labels

no code implementations18 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.

Stochastic Block Model

Clustered Graph Matching for Label Recovery and Graph Classification

no code implementations6 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.

Classification Graph Classification +1

Subgraph nomination: Query by Example Subgraph Retrieval in Networks

no code implementations29 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.

Recommendation Systems Retrieval

The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks

no code implementations1 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.

Time Series

On the role of features in vertex nomination: Content and context together are better (sometimes)

no code implementations29 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.

Information Retrieval Retrieval

Graph matching between bipartite and unipartite networks: to collapse, or not to collapse, that is the question

1 code implementation5 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.

Graph Matching

Vertex Nomination, Consistent Estimation, and Adversarial Modification

no code implementations6 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.

Graph Embedding

Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks

no code implementations26 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.

Graph Matching

Matched Filters for Noisy Induced Subgraph Detection

no code implementations6 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.

Graph Matching

Vertex nomination: The canonical sampling and the extended spectral nomination schemes

no code implementations14 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~$\mathcal{L}^P$.

Stochastic Block Model

On consistent vertex nomination schemes

no code implementations15 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 $G_2$.

Information Retrieval Retrieval

Statistical inference on random dot product graphs: a survey

no code implementations16 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.

Community Detection

On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching

no code implementations5 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.

Graph Matching Stochastic Block Model

Information Recovery in Shuffled Graphs via Graph Matching

no code implementations8 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.

Graph Clustering Graph Matching +2

Laplacian Eigenmaps from Sparse, Noisy Similarity Measurements

no code implementations12 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.

Dimensionality Reduction

Semi-External Memory Sparse Matrix Multiplication for Billion-Node Graphs

2 code implementations9 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

Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks

no code implementations18 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.

Dimensionality Reduction Graph Embedding +1

Fast Embedding for JOFC Using the Raw Stress Criterion

no code implementations11 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.

Graph Matching: Relax at Your Own Risk

no code implementations13 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.

Graph Matching

Perfect Clustering for Stochastic Blockmodel Graphs via Adjacency Spectral Embedding

no code implementations2 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.

A central limit theorem for scaled eigenvectors of random dot product graphs

no code implementations31 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.

Seeded Graph Matching

no code implementations3 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.

Graph Matching

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