Abstract
Hierarchical navigable small world (HNSW) graphs get more and more popular on large-scale nearest neighbor search tasks since the source codes were released two years ago. The attractiveness of this approach lies in its superior performance over most of the nearest neighbor search approaches as well as its genericness to various distance measures. In this paper, several comparative studies have been conducted on this search approach. The role of hierarchical structure in HNSW and the function of HNSW graph itself are investigated. We find that the hierarchical structure in HNSW could not achieve “a much better logarithmic complexity scaling” as it was claimed in the paper, particularly on high dimensional data. Moreover, we find that similar high search speed efficiency as HNSW could be achieved with the support of flat k-NN graph after graph diversification. Finally, we point out the difficulty, faced by most of the graph based search approaches, is directly linked to “curse of dimensionality”. HNSW, like other graph based approaches, is unable to address such difficulty.
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URL
https://arxiv.org/abs/1904.02077