10 citations · 31 across the 12 of their papers we have counts for
5 papers · 1 filter
Differential Privacy from Locally Adjustable Graph Algorithms: -Core Decomposition, Low Out-Degree Ordering, and Densest Subgraphs
Laxman Dhulipala, Quanquan C. Liu, Sofya Raskhodnikova +3
Differentially private algorithms allow large-scale data analytics while preserving user privacy. Designing such algorithms for graph data is gaining importance with the growth of…
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…
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 Batch-Dynamic Algorithms for -Core Decomposition and Related Graph Problems
Quanquan C. Liu, Jessica Shi, Shangdi Yu +2
Maintaining a -core decomposition quickly in a dynamic graph has important applications in network analysis. The main challenge for designing efficient exact algorithms is that…
Parallel Clique Counting and Peeling Algorithms
Jessica Shi, Laxman Dhulipala, Julian Shun
We present a new parallel algorithm for -clique counting/listing that has polylogarithmic span (parallel time) and is work-efficient (matches the work of the best sequential alg…