Dynamic Exploration Graph: A Novel Approach for Efficient Nearest Neighbor Search in Evolving Multimedia Datasets

2026-07-29

AI Summary

This work addresses the challenge of balancing efficiency and structural stability in graph-based approximate nearest neighbor search under dynamic multimedia scenarios involving frequent data insertions and deletions. The authors propose the Dynamic Exploration Graph (DEG), which introduces a novel vertex deletion strategy that preserves graph connectivity and a distribution-agnostic expansion mechanism to maintain high search accuracy and efficiency in evolving environments. Experimental results demonstrate that DEG significantly outperforms existing dynamic graph-based methods on dynamic datasets, achieving superior performance in both index construction time and query efficiency. Moreover, DEG attains state-of-the-art results even on static datasets, confirming its robustness and general applicability.

Abstract

Approximate Nearest Neighbor Search (ANNS) represents a fundamental problem in various applications (image-search, recommendation systems). While graph-based algorithms have demonstrated a good balance between search accuracy and time, handling dynamic datasets, where data points are continuously added or removed, remains a challenge. This paper introduces the Dynamic Exploration Graph (DEG), an extension of the continuous refining Exploration Graph, which retains high search efficiency for static dataset while adding essential support for dynamic data. At the core of the DEG design are two key innovations: a novel vertex deletion algorithm which guarantees graph connectivity and a data distribution-agnostic method for graph expansion. Through these mechanisms, the DEG maintains a balanced and well-connected structure, even under continuous data alterations. Empirical experiments in both streaming and online scenarios demonstrate the superior performance of the DEG, surpassing existing dynamic graph algorithms in terms of construction time and search efficiency. Although optimized for dynamic datasets, the DEG delivers results as good as current state-of-the-art approaches for static dataset, underscoring its broad applicability.

Problem

• Approximate Nearest Neighbor Search
• Dynamic Datasets
• Graph-based Algorithms
• Multimedia Data

Innovation

• Dynamic Exploration Graph
• Approximate Nearest Neighbor Search
• Graph-based ANNS
• Vertex Deletion Algorithm
• Dynamic Dataset