NSSG — High Dimensional Similarity Search with Satellite System Graph

VLDB 2024 paper presenting DIDS, a fast similarity search method using double indices and double summarizations to accelerate high-dimensional vector queries.

VLDB 2023 paper proposing LSH-APG, a method combining locality-sensitive hashing with adaptive proximity graphs for efficient index construction and approximate nearest neighbor search in high-dimensional spaces.

Starling is an I/O-efficient, disk-resident graph index framework tailored for high-dimensional vector similarity search on large data segments, supporting the scalable storage and retrieval needs of vector databases.

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.

iRangeGraph is an ANN indexing approach and accompanying implementation for range-filtering nearest neighbor search. It provides a specialized graph-based index that supports vector similarity search under range constraints, making it directly useful as a component or reference implementation for advanced vector database indexing and retrieval.

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.