69 citations · 180 across the 32 of their papers we have counts for
4 papers · 1 filter
Graph Clustering in All Parameter Regimes
Junhao Gan, David F. Gleich, Nate Veldt +2
Resolution parameters in graph clustering represent a size and quality trade-off. We address the task of efficiently solving a parameterized graph clustering objective for all valu…
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…
A Parallel Projection Method for Metric Constrained Optimization
Cameron Ruggles, Nate Veldt, David F. Gleich
Many clustering applications in machine learning and data mining rely on solving metric-constrained optimization problems. These problems are characterized by constraints…