10 citations · 23 across the 8 of their papers we have counts for
11 papers
The ParClusterers Benchmark Suite (PCBS): A Fine-Grained Analysis of Scalable Graph Clustering
Shangdi Yu, Jessica Shi, Jamison Meindl +7
We introduce the ParClusterers Benchmark Suite (PCBS) -- a collection of highly scalable parallel graph clustering algorithms and benchmarking tools that streamline comparing diffe…
Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth
Laxman Dhulipala, David Eisenstat, Jakub Łącki +2
Obtaining scalable algorithms for hierarchical agglomerative clustering (HAC) is of significant interest due to the massive size of real-world datasets. At the same time, efficient…
Scalable Community Detection via Parallel Correlation Clustering
Jessica Shi, Laxman Dhulipala, David Eisenstat +2
Graph clustering and community detection are central problems in modern data mining. The increasing need for analyzing billion-scale data calls for faster and more scalable algorit…
Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time
Laxman Dhulipala, David Eisenstat, Jakub Łącki +2
We study the widely used hierarchical agglomerative clustering (HAC) algorithm on edge-weighted graphs. We define an algorithmic framework for hierarchical agglomerative graph clus…
Design and Analysis of Bipartite Experiments under a Linear Exposure-Response Model
Christopher Harshaw, Fredrik Sävje, David Eisenstat +2
A bipartite experiment consists of one set of units being assigned treatments and another set of units for which we measure outcomes. The two sets of units are connected by a bipar…
Time-Space Trade-offs in Population Protocols
Dan Alistarh, James Aspnes, David Eisenstat +2
Population protocols are a popular model of distributed computing, in which randomly-interacting agents with little computational power cooperate to jointly perform computational t…