Search Results for author: Youngser Park

Found 23 papers, 8 papers with code

Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection

1 code implementation18 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.

Community Detection

Dynamic Network Sampling for Community Detection

no code implementations29 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.

Community Detection

Multiple Network Embedding for Anomaly Detection in Time Series of Graphs

1 code implementation23 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

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

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

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

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.

Techniques for clustering interaction data as a collection of graphs

no code implementations24 Jun 2014 Nam H. Lee, Carey Priebe, Youngser Park, I-Jeng Wang, Michael Rosen

A natural approach to analyze interaction data of form "what-connects-to-what-when" is to create a time-series (or rather a sequence) of graphs through temporal discretization (bandwidth selection) and spatial discretization (vertex contraction).

Community Detection Model Selection +1

Automatic Dimension Selection for a Non-negative Factorization Approach to Clustering Multiple Random Graphs

no code implementations24 Jun 2014 Nam H. Lee, I-Jeng Wang, Youngser Park, Care E. Priebe, Michael Rosen

We consider a problem of grouping multiple graphs into several clusters using singular value thesholding and non-negative factorization.

Model Selection

Out-of-sample Extension for Latent Position Graphs

no code implementations21 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.

General Classification Graph Embedding

On the Incommensurability Phenomenon

no code implementations9 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.

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