69 citations · 180 across the 32 of their papers we have counts for
6 papers · 1 filter
Densest Subhypergraph: Negative Supermodular Functions and Strongly Localized Methods
Yufan Huang, David F. Gleich, Nate Veldt
Dense subgraph discovery is a fundamental primitive in graph and hypergraph analysis which among other applications has been used for real-time story detection on social media and…
Faster Approximation Algorithms for Parameterized Graph Clustering and Edge Labeling
Vedangi Bengali, Nate Veldt
Graph clustering is a fundamental task in network analysis where the goal is to detect sets of nodes that are well-connected to each other but sparsely connected to the rest of the…
On the Optimal Recovery of Graph Signals
Simon Foucart, Chunyang Liao, Nate Veldt
Learning a smooth graph signal from partially observed data is a well-studied task in graph-based machine learning. We consider this task from the perspective of optimal recovery,…
Overlapping and Robust Edge-Colored Clustering in Hypergraphs
Alex Crane, Brian Lavallee, Blair D. Sullivan +1
A recent trend in data mining has explored (hyper)graph clustering algorithms for data with categorical relationship types. Such algorithms have applications in the analysis of soc…
Seven open problems in applied combinatorics
Sinan G. Aksoy, Ryan Bennink, Yuzhou Chen +10
We present and discuss seven different open problems in applied combinatorics. The application areas relevant to this compilation include quantum computing, algorithmic differentia…
Cut-matching Games for Generalized Hypergraph Ratio Cuts
Nate Veldt
Hypergraph clustering is a basic algorithmic primitive for analyzing complex datasets and systems characterized by multiway interactions, such as group email conversations, groups…