118 citations · 440 across the 36 of their papers we have counts for
18 papers · 1 filter
Concentration bounds on response-based vector embeddings of black-box generative models
Aranyak Acharyya, Joshua Agterberg, Youngser Park +1
Generative models, such as large language models or text-to-image diffusion models, can generate relevant responses to user-given queries. Response-based vector embeddings of gener…
Semisupervised regression in latent structure networks on unknown manifolds
Aranyak Acharyya, Joshua Agterberg, Michael W. Trosset +2
Random graphs are increasingly becoming objects of interest for modeling networks in a wide range of applications. Latent position random graph models posit that each node is assoc…
Dynamic Silos: Increased Modularity in Intra-organizational Communication Networks during the Covid-19 Pandemic
Tiona Zuzul, Emily Cox Pahnke, Jonathan Larson +9
Workplace communications around the world were drastically altered by Covid-19, related work-from-home orders, and the rise of remote work. To understand these shifts, we analyzed…
Vertex Nomination, Consistent Estimation, and Adversarial Modification
Joshua Agterberg, Youngser Park, Jonathan Larson +3
Given a pair of graphs and and a vertex set of interest in , the vertex nomination (VN) problem seeks to find the corresponding vertices of interest in (if t…
Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering
Congyuan Yang, Carey E. Priebe, Youngser Park +1
Our problem of interest is to cluster vertices of a graph by identifying underlying community structure. Among various vertex clustering approaches, spectral clustering is one of t…
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Carey E. Priebe, Youngser Park, Joshua T. Vogelstein +6
Clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering - clustering the vertices of a graph based o…
Matched Filters for Noisy Induced Subgraph Detection
Daniel L. Sussman, Youngser Park, Carey E. Priebe +1
The problem of finding the vertex correspondence between two noisy graphs with different number of vertices where the smaller graph is still large has many applications in social n…
Semiparametric spectral modeling of the Drosophila connectome
Carey E. Priebe, Youngser Park, Minh Tang +8
We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussia…
Vertex Nomination Via Seeded Graph Matching
Heather G. Patsolic, Youngser Park, Vince Lyzinski +1
Consider two networks on overlapping, non-identical vertex sets. Given vertices of interest in the first network, we seek to identify the corresponding vertices, if any exist, in t…
Community Detection and Classification in Hierarchical Stochastic Blockmodels
Vince Lyzinski, Minh Tang, Avanti Athreya +2
We propose a robust, scalable, integrated methodology for community detection and community comparison in graphs. In our procedure, we first embed a graph into an appropriate Eucli…
Fast Embedding for JOFC Using the Raw Stress Criterion
Vince Lyzinski, Youngser Park, Carey E. Priebe +1
The Joint Optimization of Fidelity and Commensurability (JOFC) manifold matching methodology embeds an omnibus dissimilarity matrix consisting of multiple dissimilarities on the sa…
Techniques for clustering interaction data as a collection of graphs
Nam H. Lee, Carey Priebe, Youngser Park +2
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 (ban…
Automatic Dimension Selection for a Non-negative Factorization Approach to Clustering Multiple Random Graphs
Nam H. Lee, I-Jeng Wang, Youngser Park +2
We consider a problem of grouping multiple graphs into several clusters using singular value thesholding and non-negative factorization. We derive a model selection information cri…
Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability
Heather Patsolic, Sancar Adali, Joshua T. Vogelstein +4
We present a novel approximate graph matching algorithm that incorporates seeded data into the graph matching paradigm. Our Joint Optimization of Fidelity and Commensurability (JOF…
Spectral Clustering for Divide-and-Conquer Graph Matching
Vince Lyzinski, Daniel L. Sussman, Donniell E. Fishkind +5
We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs. Our algorithm combines spectral graph embedding with e…
Out-of-sample Extension for Latent Position Graphs
Minh Tang, Youngser Park, Carey E. Priebe
We consider the problem of vertex classification for graphs constructed from the latent position model. It was shown previously that the approach of embedding the graphs into some…
On the Incommensurability Phenomenon
Donniell E. Fishkind, Cencheng Shen, Youngser Park +1
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…
Anomaly Detection in Time Series of Graphs using Fusion of Graph Invariants
Youngser Park, Carey E. Priebe, Abdou Youssef
Given a time series of graphs G(t) = (V, E(t)), t = 1, 2, ..., where the fixed vertex set V represents "actors" and an edge between vertex u and vertex v at time t (uv \in E(t)) re…