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Distribution-Aware Exploration for Adaptive HNSW Search
Summary: Query-adaptive HNSW exploration factor (Ada-ef) for ANNS: statistically model query–data similarity to set per-query ef under a declarative target recall. Distribution-aware, update-friendly, avoids over/under-searching; 4x latency, 50x compute, 100x memory gains.
(summarized by gpt-5.4-mini on Apr 11 2026)
Paper ID
h1bd16fe7d0ba040e
Venue
SIGMOD
Year
2026
Pagerank
5.154741e-05
Overall Rank
9,568 | 35.70%
DOI
10.1145/3786639
PDF
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(CC BY 4.0)
Incoming Non-self Citations Over Time
BibTeX Citation
@inproceedings{zhang_sigmod26,
title = {{Distribution-Aware Exploration for Adaptive HNSW Search}},
author = {Zhang, Chao and Miller, Renée J.},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786639},
url = {https://dl.acm.org/doi/10.1145/3786639},
year = {2026}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
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A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search 2021
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Kuzu* Graph Database Management System 2023
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TigerVector: Supporting Vector Search in Graph Databases for Advanced RAGs 2025
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DARTH: Declarative Recall Through Early Termination for Approximate Nearest Neighbor Search 2026
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