FANNG — Fast Approximate Nearest Neighbour Graphs

Foundational paper introducing the navigable small world (NSW) graph algorithm for approximate nearest neighbor search, which became the basis for widely-used graph-based ANN methods including HNSW.

Cagra provides highly parallel graph construction and approximate nearest neighbor search for GPUs, supporting large-scale vector database operations and efficient similarity search.

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.

Technical article by Yury Malkov covering approximate nearest neighbor search applications in recommender systems. Discusses how ANN algorithms accelerate candidate generation in large-scale recommendation pipelines.

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.