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    3. NSW — Approximate Nearest Neighbor Search on Navigable Small World Graphs

    NSW — Approximate Nearest Neighbor Search on Navigable Small World Graphs

    Foundational paper introducing the navigable small world (NSW) graph algorithm for approximate nearest neighbor search, which became the basis for widely-used graph-based ANN methods including HNSW.

    Overview

    Approximate nearest neighbor algorithm based on navigable small world graphs (NSW) introduced graph-based approaches to ANN search using navigable small world network properties.

    Key Contributions

    • Proposed graph-based ANN search using navigable small world network topology
    • Established foundation for hierarchical variants like HNSW
    • Demonstrates that greedy routing on small-world graphs yields logarithmic search complexity
    • Influential IS2014 publication by Malkov et al.

    Publication

    • Venue: IS 2014
    • Authors: Malkov et al.
    • Abbreviation: NSW

    Impact

    NSW laid the groundwork for subsequent graph-based ANN methods, most notably HNSW, which became one of the most widely deployed ANN algorithms in production vector databases.

    Surveys

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    Information

    Websitegithub.com
    PublishedApr 4, 2026

    Categories

    1 Item
    Research Papers & Surveys

    Tags

    3 Items
    #graph-index#ann#approximate-nearest-neighbor

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    ARKGraph — All-Range Approximate K-Nearest-Neighbor Graph

    VLDB 2023 paper proposing ARKGraph, a graph-based method for all-range approximate k-nearest neighbor search that adapts to various recall requirements.

    EFANNA — Extremely Fast Approximate Nearest Neighbor Search Based on kNN Graph

    Paper proposing EFANNA, an extremely fast approximate nearest neighbor search algorithm based on kNN graph construction. The method introduces an efficient approximate kNN graph building approach and a search algorithm that achieves state-of-the-art query performance.

    FANNG — Fast Approximate Nearest Neighbour Graphs

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    HNSW — Efficient and Robust ANNS Using Hierarchical Navigable Small World Graphs

    Foundational TPAMI 2018 paper introducing Hierarchical Navigable Small World (HNSW) graphs, one of the most widely adopted approximate nearest neighbor search algorithms. The hierarchical multi-layer graph structure enables logarithmic-time search with high recall.

    Learning to Route in Similarity Graphs

    ICML 2019 paper introducing a learned routing approach for similarity graphs, using machine learning to guide greedy search traversal in graph-based approximate nearest neighbor search.