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Conference Papers Year : 2017

Beyond Highway Dimension: Small Distance Labels Using Tree Skeletons


The goal of a hub-based distance labeling scheme for a network G = (V, E) is to assign a small subset S(u) ⊆ V to each node u ∈ V, in such a way that for any pair of nodes u, v, the intersection of hub sets S(u) ∩ S(v) contains a node on the shortest uv-path. The existence of small hub sets, and consequently efficient shortest path processing algorithms, for road networks is an empirical observation. A theoretical explanation for this phenomenon was proposed by Abraham et al. (SODA 2010) through a network parameter they called highway dimension, which captures the size of a hitting set for a collection of shortest paths of length at least r intersecting a given ball of radius 2r. In this work, we revisit this explanation, introducing a more tractable (and directly comparable) parameter based solely on the structure of shortest-path spanning trees, which we call skeleton dimension. We show that skeleton dimension admits an intuitive definition for both directed and undirected graphs, provides a way of computing labels more efficiently than by using highway dimension, and leads to comparable or stronger theoretical bounds on hub set size.
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Dates and versions

hal-01359084 , version 1 (01-09-2016)
hal-01359084 , version 2 (10-12-2016)



Adrian Kosowski, Laurent Viennot. Beyond Highway Dimension: Small Distance Labels Using Tree Skeletons. SODA 2017 - 28th ACM-SIAM Symposium on Discrete Algorithms, Jan 2017, Barcelona, Spain. ⟨hal-01359084v2⟩
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