10 citations · 31 across the 12 of their papers we have counts for
12 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…
Density and Affinity Dependent Social Segregation and Arbitrage Equilibrium in a Multi-class Schelling Game
Venkat Venkatasubramanian, Jessica Shi, Leo Goldman +2
Contrary to the widely believed hypothesis that larger, denser cities promote socioeconomic mixing, a recent study (Nilforoshan et al. 2023) reports the opposite behavior, i.e. mor…
Parallel Algorithms for Hierarchical Nucleus Decomposition
Jessica Shi, Laxman Dhulipala, Julian Shun
Nucleus decompositions have been shown to be a useful tool for finding dense subgraphs. The coreness value of a clique represents its density based on the number of other cliques i…
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…
Theoretically and Practically Efficient Parallel Nucleus Decomposition
Jessica Shi, Laxman Dhulipala, Julian Shun
This paper studies the nucleus decomposition problem, which has been shown to be useful in finding dense substructures in graphs. We present a novel parallel algorithm that is effi…
An Attempt to Generate Code for Symmetric Tensor Computations
Jessica Shi, Stephen Chou, Fredrik Kjolstad +1
This document describes an attempt to develop a compiler-based approach for computations with symmetric tensors. Given a computation and the symmetries of its input tensors, we der…
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 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…
Parallel Algorithms for Butterfly Computations
Jessica Shi, Julian Shun
Butterflies are the smallest non-trivial subgraph in bipartite graphs, and therefore having efficient computations for analyzing them is crucial to improving the quality of certain…