Locality-Sensitive Indexing for Graph-Based ANNS

ICDE 2023 and TKDE 2023 papers introducing DB-LSH, a locality-sensitive hashing approach with query-based dynamic bucketing for efficient approximate nearest neighbor search.

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.

VLDB 2021 paper introducing MP-RW-LSH, an efficient multi-probe locality-sensitive hashing solution for A1-norm (Manhattan distance) approximate nearest neighbor search.

VLDB 2022 paper introducing PM-LSH, an in-memory locality-sensitive hashing framework for high-dimensional approximate nearest neighbor and closest pair search with strong accuracy guarantees.

VLDB 2015 paper introducing QALSH, a query-aware locality-sensitive hashing scheme that improves retrieval accuracy by dynamically adjusting hash functions based on query characteristics.

VLDB 2025 paper proposing efficient level navigation with shortcuts for accelerating approximate nearest neighbor search in hierarchical graph indexes, improving traversal speed across multi-layer graph structures.