  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.
