For approximate nearest neighbor search, graph-based algorithms have shown to offer the best trade-off between accuracy and search time. We propose the Dynamic Exploration Graph (DEG) which significantly outperforms existing algorithms in terms of search and exploration efficiency by combining two new ideas: First, a single undirected even regular graph is incrementally built by partially replacing existing edges to integrate new vertices and to update old neighborhoods at the same time. Secondly, an edge optimization algorithm is used to continuously improve the quality of the graph. Combining this ongoing refinement with the graph construction process leads to a well-organized graph structure at all times, resulting in: (1) increased search efficiency, (2) predictable index size, (3) guaranteed connectivity and therefore reachability of all vertices, and (4) a dynamic graph structure. In addition we investigate how well existing graph-based search systems can handle indexed queries where the seed vertex of a search is the query itself. Such exploration tasks, despite their good starting point, are not necessarily easy. High efficiency in approximate nearest neighbor search (ANNS) does not automatically imply good performance in exploratory search. Extensive experiments show that our new Dynamic Exploration Graph outperforms existing algorithms significantly for indexed and unindexed queries.

翻译：对于近似最近邻搜索，基于图的算法在精度与搜索时间之间展现出最佳平衡。我们提出了动态探索图（DEG），通过结合两种新思路，在搜索和探索效率方面显著超越现有算法：第一，通过部分替换现有边来逐步构建无向偶正则图，同时整合新顶点并更新旧邻域；第二，采用边优化算法持续提升图的质量。将这种持续优化与图构建过程相结合，始终保持图结构的良好组织性，从而带来：（1）搜索效率提升，（2）可预测的索引规模，（3）连通性保障进而确保所有顶点可达，（4）动态图结构。此外，我们探究了现有基于图的搜索系统如何处理索引查询——即搜索的种子顶点为查询本身的情况。尽管具有良好起点，此类探索任务未必简单。近似最近邻搜索（ANNS）的高效率并不自动意味着探索性搜索的高性能。大量实验表明，我们提出的新型动态探索图在索引查询与非索引查询中均显著优于现有算法。