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

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