Learning to Route in Similarity Graphs

SIGKDD 2025 paper proposing adaptive awareness capabilities for graph-based approximate nearest neighbor search, enabling the search algorithm to dynamically adjust its strategy based on local graph characteristics and query properties.

VLDB 2023 paper presenting ELPIS, a graph-based similarity search approach that combines graph indexing with learning-based techniques for scalable data science applications on large datasets.

SIGMOD 2020 paper proposing learned adaptive early termination for approximate nearest neighbor search, using machine learning to predict when to stop searching, balancing accuracy and latency dynamically.

Foundational paper introducing the navigable small world (NSW) graph algorithm for approximate nearest neighbor search, which became the basis for widely-used graph-based ANN methods including HNSW.

Paper from 2024 proposing a probabilistic routing approach for graph-based approximate nearest neighbor search, introducing probability models to guide search traversal on proximity graphs.

VLDB 2024 paper proposing RoarGraph, a projected bipartite graph structure for efficient cross-modal approximate nearest neighbor search. The method addresses the challenges of searching across different modalities (e.g., text, image) using graph-based indexing.