Graph Based Approximate Nearest Neighbor

Graph-based approximate nearest neighbor (ANN) search aims to efficiently find data points closest to a query in high-dimensional spaces, a crucial task in numerous applications like recommendation systems and large language models. Current research focuses on improving the speed and accuracy of graph-based ANN algorithms, such as HNSW and DiskANN, particularly for challenging scenarios involving out-of-distribution queries and massive datasets. This involves developing novel graph structures, optimizing search strategies (e.g., probabilistic routing, adaptive entry point selection), and exploring parallel and storage-efficient implementations, including leveraging computational storage platforms. These advancements significantly impact the scalability and efficiency of similarity search in various data-intensive applications.