mlsanityData to 7 Oct 2026

General and Practical Tuning Method for Off-the-Shelf Graph-Based Index: SISAP Indexing Challenge Report by Team UTokyo

arXiv 2309.00472 ↗First posted 1 Sep 2023cs.IR cs.CV cs.DBcited by 3

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Abstract (arXiv)

Despite the efficacy of graph-based algorithms for Approximate Nearest Neighbor (ANN) searches, the optimal tuning of such systems remains unclear. This study introduces a method to tune the performance of off-the-shelf graph-based indexes, focusing on the dimension of vectors, database size, and entry points of graph traversal. We utilize a black-box optimization algorithm to perform integrated tuning to meet the required levels of recall and Queries Per Second (QPS). We applied our approach to Task A of the SISAP 2023 Indexing Challenge and got second place in the 10M and 30M tracks. It improves performance substantially compared to brute force methods. This research offers a universally applicable tuning method for graph-based indexes, extending beyond the specific conditions of the competition to broader uses.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 13 references that are in the map. First posted on arXiv 1 Sep 2023.

Builds on

7 of 13 references.

References · 7

  1. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  2. 2017Billion-scale similarity search with GPUscited by 2,477
  3. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  4. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 853
  5. 2017Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graphcited by 103
  6. 2018Optimization of Indexing Based on k-Nearest Neighbor Graph for Proximity Search in High-dimensional Datacited by 20
  7. 2022Similarity search on neighbor's graphs with automatic Pareto optimal performance and minimum expected quality setups based on hyperparameter optimizationcited by 1

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Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024Theoretical and Empirical Analysis of Adaptive Entry Point Selection for Graph-based Approximate Nearest Neighbor Searchcited by 1
  2. 2025LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Tablecited by 1
  3. 2025PLASMA: A Layout-Aware Benchmark Reveals Memory Layout Matters for Graph-based ANNS on GPUcited by 0