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

    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.

    Overview

    EFANNA is an extremely fast approximate nearest neighbor search algorithm based on kNN graphs, proposed by Cong Fu and colleagues in 2016.

    Key Contributions

    • Introduces an efficient approximate kNN graph construction algorithm
    • Proposes a novel tree-based initial neighborhood selection for graph construction
    • Search algorithm achieves high recall with low latency compared to contemporaries
    • Demonstrates strong performance across multiple benchmark datasets

    Publication

    • Year: 2016
    • Authors: Fu, Wei, Zhang et al.
    • Abbreviation: EFANNA

    Impact

    EFANNA's graph construction techniques influenced subsequent graph-based methods and contributed to the development of more efficient ANNS algorithms. The work was extended in several follow-up publications by the same research group.

    Surveys

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    Information

    Websiteieeexplore.ieee.org
    PublishedApr 4, 2026

    Categories

    1 Item
    Research Papers & Surveys

    Tags

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

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    Accelerating Graph Indexing for ANNS on Modern CPUs

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