69 citations · 180 across the 32 of their papers we have counts for
9 papers · 1 filter
fauci-email: a json digest of Anthony Fauci's released emails
Austin R. Benson, Nate Veldt, David F. Gleich
A collection of over 3000 pages of emails sent by Anthony Fauci and his staff were released in an effort to understand the United States government response to the COVID-19 pandemi…
Combinatorial Characterizations and Impossibilities for Higher-order Homophily
Nate Veldt, Austin R. Benson, Jon Kleinberg
Homophily is the seemingly ubiquitous tendency for people to connect and interact with other individuals who are similar to them. This is a well-documented principle and is fundame…
Generative hypergraph clustering: from blockmodels to modularity
Philip S. Chodrow, Nate Veldt, Austin R. Benson
Hypergraphs are a natural modeling paradigm for a wide range of complex relational systems. A standard analysis task is to identify clusters of closely related or densely interconn…
Strongly Local Hypergraph Diffusions for Clustering and Semi-supervised Learning
Meng Liu, Nate Veldt, Haoyu Song +2
Hypergraph-based machine learning methods are now widely recognized as important for modeling and using higher-order and multiway relationships between data objects. Local hypergra…
Hypergraph Clustering for Finding Diverse and Experienced Groups
Ilya Amburg, Nate Veldt, Austin R. Benson
When forming a team or group of individuals, we often seek a balance of expertise in a particular task while at the same time maintaining diversity of skills within each group. Her…
Clustering in graphs and hypergraphs with categorical edge labels
Ilya Amburg, Nate Veldt, Austin R. Benson
Modern graph or network datasets often contain rich structure that goes beyond simple pairwise connections between nodes. This calls for complex representations that can capture, f…
Learning Resolution Parameters for Graph Clustering
Nate Veldt, David F. Gleich, Anthony Wirth
Finding clusters of well-connected nodes in a graph is an extensively studied problem in graph-based data analysis. Because of its many applications, a large number of distinct gra…
Flow-Based Local Graph Clustering with Better Seed Set Inclusion
Nate Veldt, Christine Klymko, David Gleich
Flow-based methods for local graph clustering have received significant recent attention for their theoretical cut improvement and runtime guarantees. In this work we present two i…
A Simple and Strongly-Local Flow-Based Method for Cut Improvement
Nate Veldt, David F. Gleich, Michael W. Mahoney
Many graph-based learning problems can be cast as finding a good set of vertices nearby a seed set, and a powerful methodology for these problems is based on maximum flows. We intr…