3 accepted papers
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 cost of a high space consumption for label storage. Landmark-based A* search requires less space but answers queri…
A k nearest neighbors (kNN) query finds k closest points-of-interest (POIs) from an agent's location. In this paper, we study a natural extension of the kNN query for multiple agents, namely, the Aggregate k Nearest Neighbors (AkNN) query. An AkNN query retrieves k POIs with the smallest aggregate…
Graph sparsification has been used to improve the computational cost of learning over graphs, e.g., Laplacian-regularized estimation and graph semi-supervised learning (SSL). However, when graphs vary over time, repeated sparsification requires polynomial order computational cost per update. We prop…