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    3. NSSG — High Dimensional Similarity Search with Satellite System Graph

    NSSG — High Dimensional Similarity Search with Satellite System Graph

    Paper proposing the Satellite System Graph (NSSG) approach for high dimensional similarity search, emphasizing efficiency, scalability, and unindexed query compatibility. Published in TPAMI 2021 by Fu et al.

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    Websiteieeexplore.ieee.org
    PublishedApr 4, 2026

    Categories

    1 Item
    Research Papers & Surveys

    Tags

    3 Items
    #graph-index#similarity-search#high-dimensional

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    Starling

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    IDEA

    IDEA is an inverted, deduplication-aware index structure designed to improve storage efficiency and query performance for similarity search workloads. It is implemented as research code and targets high-dimensional vector and content-addressable data, making it relevant to large-scale vector database and ANN indexing systems.

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    NSG is an approximate nearest neighbor search algorithm based on a sparse navigable graph structure designed for high-dimensional vector similarity search. The reference implementation provides a graph-based ANN index that can be integrated into custom vector retrieval systems.

    Overview

    NSSG (Navigable Satellite System Graph) is a graph-based approximate nearest neighbor search method designed for high-dimensional data with emphasis on efficiency, scalability, and compatibility with unindexed queries.

    Key Contributions

    • Proposes a satellite system graph structure for similarity search
    • Addresses scalability challenges in high-dimensional spaces
    • Supports both indexed and unindexed query modes
    • Demonstrates strong empirical performance on large-scale datasets
    • Published in TPAMI 2021

    Publication

    • Venue: IEEE TPAMI 2021
    • Authors: Fu et al.
    • Abbreviation: NSSG

    Features

    • Efficient graph construction with satellite system topology
    • Scalable to large datasets with high-dimensional vectors
    • Supports unindexed queries without requiring full index rebuild
    • Maintains high recall across diverse data distributions