# On the Merge of k-NN Graph

arXiv: https://arxiv.org/abs/1908.00814 · HTML: https://mlsanity.com/paper/1908.00814/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [FGIM: a Fast Graph-based Indexes Merging Framework for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2603.21710/) (2026)

## Abstract (arXiv)

k-nearest neighbor graph is a fundamental data structure in many disciplines such as information retrieval, data-mining, pattern recognition, and machine learning, etc. In the literature, considerable research has been focusing on how to efficiently build an approximate k-nearest neighbor graph (k-NN graph) for a fixed dataset. Unfortunately, a closely related issue of how to merge two existing k-NN graphs has been overlooked. In this paper, we address the issue of k-NN graph merging in two different scenarios. In the first scenario, a symmetric merge algorithm is proposed to combine two approximate k-NN graphs. The algorithm facilitates large-scale processing by the efficient merging of k-NN graphs that are produced in parallel. In the second scenario, a joint merge algorithm is proposed to expand an existing k-NN graph with a raw dataset. The algorithm enables the incremental construction of a hierarchical approximate k-NN graph. Superior performance is attained when leveraging the hierarchy for NN search of various data types, dimensionality, and distance measures.