ParlayANN — Scalable and Deterministic Parallel Graph-Based ANNS

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.

SIGKDD 2025 paper proposing adaptive awareness capabilities for graph-based approximate nearest neighbor search, enabling the search algorithm to dynamically adjust its strategy based on local graph characteristics and query properties.

SIGMOD 2025 paper proposing optimizations for graph-based approximate nearest neighbor search indexing on modern CPU architectures, leveraging SIMD instructions and cache-aware algorithms for improved index construction performance.

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

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.

VLDB 2023 paper presenting ELPIS, a graph-based similarity search approach that combines graph indexing with learning-based techniques for scalable data science applications on large datasets.