Search Results for author:
Found 7 papers, 5 papers with code
Unleashing Graph Partitioning for Large-Scale Nearest Neighbor Search
In particular, our new routing methods enable the use of balanced graph partitioning, which is a high-quality partitioning method without a naturally associated routing algorithm.
Approximate Nearest Neighbor Search with Window Filters
We define and investigate the problem of $\textit{c-approximate window search}$: approximate nearest neighbor search where each point in the dataset has a numeric label, and the goal is to find nearest neighbors to queries within arbitrary label ranges.
TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs
Our algorithm is based on a new approach to computing $(1+\epsilon)$-approximate HAC, which is a novel combination of the nearest-neighbor chain algorithm and the notion of $(1+\epsilon)$-approximate HAC.
ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms
Our algorithms are deterministic and achieve high scalability across a diverse set of challenging datasets.
Scalable Community Detection via Parallel Correlation Clustering
Our empirical evaluation shows that this framework improves the state-of-the-art trade-offs between speed and quality of scalable community detection.
Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time
For this variant, this is the first exact algorithm that runs in subquadratic time, as long as $m=n^{2-\epsilon}$ for some constant $\epsilon > 0$.
ParChain: A Framework for Parallel Hierarchical Agglomerative Clustering using Nearest-Neighbor Chain
This paper studies the hierarchical clustering problem, where the goal is to produce a dendrogram that represents clusters at varying scales of a data set.
