AI 中文总结
该研究针对动态数据集的高效最近邻搜索问题，提出动态探索图（DEG），通过新型顶点删除算法与数据分布无关的图扩展方法，在构建时间和搜索效率上优于现有动态图算法，且适配静态场景，适用性广泛。
AI 中文摘要
近似最近邻搜索（ANNS）是图像搜索、推荐系统等各类应用中的基础问题。尽管基于图的算法在搜索准确率与时间间取得了良好平衡，但处理数据点持续增删的动态数据集仍是一项挑战。本文提出动态探索图（DEG），它是连续优化探索图的扩展，在保留静态数据集高搜索效率的同时，新增了对动态数据的核心支持。DEG设计的核心是两项关键创新：保证图连通性的新型顶点删除算法，以及与数据分布无关的图扩展方法。通过这些机制，即便在数据持续变更下，DEG仍能维持平衡且连通性良好的结构。在流场景与在线场景中的实证实验表明，DEG性能优异，在构建时间与搜索效率上优于现有动态图算法。尽管针对动态数据集优化，DEG的表现与当前静态数据集的最优方法相当，凸显了其广泛适用性。
英文摘要
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.
Journal refProc. MMM 2025, pp. 333-347
DOI：10.1007/978-981-96-2054-8_25
