69 citations · 180 across the 32 of their papers we have counts for
6 papers · 1 filter
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…
Augmented Sparsifiers for Generalized Hypergraph Cuts with Applications to Decomposable Submodular Function Minimization
Austin R. Benson, Jon Kleinberg, Nate Veldt
In recent years, hypergraph generalizations of many graph cut problems have been introduced and analyzed as a way to better explore and understand complex systems and datasets char…
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…
Parameterized Correlation Clustering in Hypergraphs and Bipartite Graphs
Nate Veldt, Anthony Wirth, David F. Gleich
Motivated by applications in community detection and dense subgraph discovery, we consider new clustering objectives in hypergraphs and bipartite graphs. These objectives are param…
Minimizing Localized Ratio Cut Objectives in Hypergraphs
Nate Veldt, Austin R. Benson, Jon Kleinberg
Hypergraphs are a useful abstraction for modeling multiway relationships in data, and hypergraph clustering is the task of detecting groups of closely related nodes in such data. G…
Hypergraph Cuts with General Splitting Functions
Nate Veldt, Austin R. Benson, Jon Kleinberg
The minimum - cut problem in graphs is one of the most fundamental problems in combinatorial optimization, and graph cuts underlie algorithms throughout discrete mathematics,…