A Note on Graph-Based Nearest Neighbor Search

TL;DR

This paper investigates why graph-based nearest neighbor search algorithms are effective, revealing that local clustering properties significantly influence their efficiency and accuracy.

Contribution

It introduces the concept that local clustering coefficient impacts search performance and analyzes the two-phase search process in relation to the maximum strongly connected component.

Findings

High clustering coefficient correlates with larger maximum SCCs.

The two-phase search algorithm guarantees traversal of the maximum SCC.

Empirical results validate the impact of clustering on search efficiency.

Abstract

Nearest neighbor search has found numerous applications in machine learning, data mining and massive data processing systems. The past few years have witnessed the popularity of the graph-based nearest neighbor search paradigm because of its superiority over the space-partitioning algorithms. While a lot of empirical studies demonstrate the efficiency of graph-based algorithms, not much attention has been paid to a more fundamental question: why graph-based algorithms work so well in practice? And which data property affects the efficiency and how? In this paper, we try to answer these questions. Our insight is that "the probability that the neighbors of a point o tends to be neighbors in the KNN graph" is a crucial data property for query efficiency. For a given dataset, such a property can be qualitatively measured by clustering coefficient of the KNN graph. To show how clustering coefficient affects the performance, we identify that, instead of the global connectivity, the local connectivity around some given query q has more direct impact on recall. Specifically, we observed that high clustering coefficient makes most of the k nearest neighbors of q sit in a maximum strongly connected component (SCC) in the graph. From the algorithmic point of view, we show that the search procedure is actually composed of two phases - the one outside the maximum SCC and the other one in it, which is different from the widely accepted single or multiple paths search models. We proved that the commonly used graph-based search algorithm is guaranteed to traverse the maximum SCC once visiting any point in it. Our analysis reveals that high clustering coefficient leads to large size of the maximum SCC, and thus provides good answer quality with the help of the two-phase search procedure. Extensive empirical results over a comprehensive collection of datasets validate our findings.