10 citations · 31 across the 12 of their papers we have counts for
5 papers · 1 filter
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…