Vector data is prevalent across business and scientific applications, and its popularity is growing with the proliferation of learned embeddings. Vector data collections often reach billions of vectors with thousands of dimensions, thus, increasing the complexity of their analysis. Vector search is the backbone of many critical analytical tasks, and graph-based methods have become the best choice for analytical tasks that do not require guarantees on the quality of the answers. We briefly survey in-memory graph-based vector search, outline the chronology of the different methods and classify them according to five main design paradigms: seed selection, incremental insertion, neighborhood propagation, neighborhood diversification, and divide-and-conquer. We conduct an exhaustive experimental evaluation of twelve state-of-the-art methods on seven real data collections, with sizes up to 1 billion vectors. We share key insights about the strengths and limitations of these methods; e.g., the best approaches are typically based on incremental insertion and neighborhood diversification, and the choice of the base graph can hurt scalability. Finally, we discuss open research directions, such as the importance of devising more sophisticated data-adaptive seed selection and diversification strategies.

The recent popularity of learned embeddings has fueled the growth of massive collections of high-dimensional (high-d) vectors that model complex data. Finding similar vectors in these collections is at the core of many important and practical data science applications. The data series community has developed tree-based similarity search techniques that outperform state-of-the-art methods on large collections of both data series and generic high-d vectors, on all scenarios except for no-guarantees ng -approximate search, where graph-based approaches designed by the high-d vector community achieve the best performance. However, building graph-based indexes is extremely expensive both in time and space. In this paper, we bring these two worlds together, study the corresponding solutions and their performance behavior, and propose ELPIS, a new strong baseline that takes advantage of the best features of both to achieve a superior performance in terms of indexing and ng-approximate search in-memory. ELPIS builds the index 3x-8x faster than competitors, using 40% less memory. It also achieves a high recall of 0.99, up to 2x faster than the state-of-the-art methods, and answers 1-NN queries up to one order of magnitude faster.