ReHub. Extending Hub Labels for Reverse k-Nearest Neighbor Queries on Large-Scale networks

TL;DR

ReHub is a new main-memory algorithm that extends hub labeling to efficiently handle reverse k-nearest neighbor queries on large-scale networks, offering minimal preprocessing and fast query responses.

Contribution

ReHub introduces a novel extension of hub labeling techniques specifically designed for reverse k-nearest neighbor queries on large-scale graphs.

Findings

ReHub outperforms existing methods in speed and efficiency.

Requires minimal preprocessing for large-scale networks.

Provides fast query times for RkNN queries.

Abstract

Quite recently, the algorithmic community has focused on solving multiple shortest-path query problems beyond simple vertex-to-vertex queries, especially in the context of road networks. Unfortunately, this research cannot be generalized for large-scale graphs, e.g., social or collaboration networks, or to efficiently answer Reverse k-Nearest Neighbor (RkNN) queries, which are of practical relevance to a wide range of applications. To remedy this, we propose ReHub, a novel main-memory algorithm that extends the Hub Labeling technique to efficiently answer RkNN queries on large-scale networks. Our experimentation will show that ReHub is the best overall solution for this type of queries, requiring only minimal preprocessing and providing very fast query times.