We consider the point-to-point (approximate) shortest-path query problem, which is the
following generalization of the classical single-source (SSSP) and all-pairs shortest-path …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

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 …

In a multi-armed bandit (MAB) problem, an online algorithm makes a sequence of choices.
In each round it chooses from a time-invariant set of alternatives and receives the payoff …

A shortest-path algorithm finds a path containing the minimal cost between two vertices in a
graph. A plethora of shortest-path algorithms is studied in the literature that span across …

In this paper we study approximate landmark-based methods for point-to-point distance
estimation in very large networks. These methods involve selecting a subset of nodes as …

Graph-based semisupervised learning (GSSL) provides a promising paradigm for modeling
the manifold structures that may exist in massive data sources in high-dimensional spaces. It …

This paper introduces a lightweight, scalable and accurate framework, called Meridian, for
performing node selection based on network location. The framework consists of an overlay …

We study the ALT algorithm [19] for the point-to-point shortest path problem in the context of
road networks. We suggest improvements to the algorithm itself and to its preprocessing …

In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a
sequence of trials to maximize the total payoff of the chosen strategies. While the …