10 citations · 16 across the 7 of their papers we have counts for
16 papers
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…
Parallel Graph Algorithms in Constant Adaptive Rounds: Theory meets Practice
Soheil Behnezhad, Laxman Dhulipala, Hossein Esfandiari +3
We study fundamental graph problems such as graph connectivity, minimum spanning forest (MSF), and approximate maximum (weight) matching in a distributed setting. In particular, we…
Faster DBSCAN via subsampled similarity queries
Heinrich Jiang, Jennifer Jang, Jakub Łącki
DBSCAN is a popular density-based clustering algorithm. It computes the -neighborhood graph of a dataset and uses the connected components of the high-degree nodes to decide the…
Fully Dynamic Matching: Beating 2-Approximation in Update Time
Soheil Behnezhad, Jakub Łącki, Vahab Mirrokni
In fully dynamic graphs, we know how to maintain a 2-approximation of maximum matching extremely fast, that is, in polylogarithmic update time or better. In a sharp contrast and de…
Near-Optimal Massively Parallel Graph Connectivity
Soheil Behnezhad, Laxman Dhulipala, Hossein Esfandiari +2
Identifying the connected components of a graph, apart from being a fundamental problem with countless applications, is a key primitive for many other algorithms. In this paper, we…