Unleashing Graph Partitioning for Large-Scale Nearest Neighbor Search
Summary: Modular, fast routing for distributed ANNS—LSH-based (provable guarantees) and clustering-based (better empirical recall)—decoupling routing from partitioning so any partitioner can be used. Enables balanced graph partitioning at scale, yielding up to 1.72× QPS at 90% 10-recall on billion-scale datasets. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Lars Gottesbüren (Karlsruhe Institute of Technology)
- 2. Laxman Dhulipala (Google; University of Maryland)
- 3. Rajesh Jayaram (Google)
- 4. Jakub Łącki (Google)
BibTeX Citation
@article{gottesburen_vldb25,
title = {{Unleashing Graph Partitioning for Large-Scale Nearest Neighbor Search}},
author = {Gottesbüren, Lars and Dhulipala, Laxman and Jayaram, Rajesh and Łącki, Jakub},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {6},
pages = {1649--1662},
doi = {10.14778/3725688.3725696},
url = {https://doi.org/10.14778/3725688.3725696},
year = {2025}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 93 | Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph | 2019 | VLDB | 0.00034701237 |
| 287 | Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search | 2007 | VLDB | 0.00022323585 |
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