Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs
This work addresses the need for efficient and robust nearest neighbor search in metric spaces, offering a scalable solution for applications like information retrieval and machine learning, though it builds incrementally on existing graph-based methods.
The authors tackled the problem of approximate nearest neighbor search by introducing Hierarchical Navigable Small World (HNSW) graphs, which achieve logarithmic complexity and outperform previous state-of-the-art vector-only approaches in performance evaluations.
We present a new approach for the approximate K-nearest neighbor search based on navigable small world graphs with controllable hierarchy (Hierarchical NSW, HNSW). The proposed solution is fully graph-based, without any need for additional search structures, which are typically used at the coarse search stage of the most proximity graph techniques. Hierarchical NSW incrementally builds a multi-layer structure consisting from hierarchical set of proximity graphs (layers) for nested subsets of the stored elements. The maximum layer in which an element is present is selected randomly with an exponentially decaying probability distribution. This allows producing graphs similar to the previously studied Navigable Small World (NSW) structures while additionally having the links separated by their characteristic distance scales. Starting search from the upper layer together with utilizing the scale separation boosts the performance compared to NSW and allows a logarithmic complexity scaling. Additional employment of a heuristic for selecting proximity graph neighbors significantly increases performance at high recall and in case of highly clustered data. Performance evaluation has demonstrated that the proposed general metric space search index is able to strongly outperform previous opensource state-of-the-art vector-only approaches. Similarity of the algorithm to the skip list structure allows straightforward balanced distributed implementation.