We survey recent advances in algorithms for route planning in transportation networks. For
road networks, we show that one can compute driving directions in milliseconds or less even …

Shortest path distance retrieval is a core component in many important applications. For a
decade, hub labeling (HL) techniques have been considered as a practical solution with fast …

Answering the shortest-path distance between two arbitrary locations is a fundamental
problem in road networks. Labelling-based solutions are the current state-of-the-arts to …

Point-to-Point Shortest Distance (PPSD) query is a crucial primitive in graph database
applications. Hub labeling algorithms compute a labeling that converts a PPSD query into a …

Hub Labeling and A* are two well-established algorithms for shortest path computation in
large graphs. Hub Labeling offers excellent query times for distance computation, but at the …

We introduce a novel approach to Compressed Path Databases, space efficient oracles
used to very quickly identify the first edge on a shortest path. Our algorithm achieves query …

To help compute shortest path distances over large graphs efficiently, 2-hop labeling has
emerged as a major tool, with Pruned Landmark Labeling (PPL) as a popular algorithm. This …

Existing message passing-based and transformer-based graph neural networks (GNNs)
cannot satisfy requirements for learning representative graph embeddings due to restricted …

Computing shortest-path distances is a fundamental primitive in the context of graph data
mining, since this kind of information is essential in a broad range of prominent applications …

There has been significant success in designing highly efficient algorithms for distance and
shortest-path queries in recent years; many of the state-of-the-art algorithms use the hub …