112 citations · 370 across the 54 of their papers we have counts for
7 papers · 1 filter
Near-Optimal Differentially Private k-Core Decomposition
Laxman Dhulipala, George Z. Li, Quanquan C. Liu
Recent work by Dhulipala et al. \cite{DLRSSY22} initiated the study of the -core decomposition problem under differential privacy via a connection between low round/depth distri…
Practical Parallel Algorithms for Near-Optimal Densest Subgraphs on Massive Graphs
Pattara Sukprasert, Quanquan C. Liu, Laxman Dhulipala +1
The densest subgraph problem has received significant attention, both in theory and in practice, due to its applications in problems such as community detection, social network ana…
TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs
Laxman Dhulipala, Jason Lee, Jakub Łącki +1
We introduce TeraHAC, a -approximate hierarchical agglomerative clustering (HAC) algorithm which scales to trillion-edge graphs. Our algorithm is based on a new approach to…
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…
ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms
Magdalen Dobson Manohar, Zheqi Shen, Guy E. Blelloch +4
Approximate nearest-neighbor search (ANNS) algorithms are a key part of the modern deep learning stack due to enabling efficient similarity search over high-dimensional vector spac…
High-Performance and Flexible Parallel Algorithms for Semisort and Related Problems
Xiaojun Dong, Yunshu Wu, Zhongqi Wang +3
Semisort is a fundamental algorithmic primitive widely used in the design and analysis of efficient parallel algorithms. It takes input as an array of records and a function extrac…
Towards Lightweight and Automated Representation Learning System for Networks
Yuyang Xie, Jiezhong Qiu, Laxman Dhulipala +4
We propose LIGHTNE 2.0, a cost-effective, scalable, automated, and high-quality network embedding system that scales to graphs with hundreds of billions of edges on a single machin…