Highway dimension
The highway dimension is a
Definitions
Several definitions of the highway dimension exist.
Definition 1
The original definition[1] of the highway dimension measures the sparseness of a hub set of shortest paths contained within a ball of radius :
The highway dimension of is the smallest integer such that for any radius and any node there is a
hitting setof size at most for all shortest paths of length more than for which .
A variant of this definition uses balls of radius for some constant . Choosing a constant greater than 4 implies additional structural properties of graphs of bounded highway dimension, which can be exploited algorithmically.[5]
Definition 2
A subsequent definition[6] of the highway dimension measures the sparseness of a hub set of shortest paths intersecting a ball of radius :
The highway dimension of is the smallest integer such that for any radius and any node there is a
hitting setof size at most for all shortest paths of length more than and at most for which .
This definition is weaker than the first, i.e., every graph of highway dimension also has highway dimension , but not vice versa.[5]
Definition 3
For the third definition[7] of the highway dimension we introduce the notion of a "witness path": for a given radius , a shortest path has an -witness path if has length more than and can be obtained from by adding at most one vertex to either end of (i.e., has at most 2 vertices more than and these additional vertices are incident to ). Note that may be shorter than but is contained in , which has length more than .
The highway dimension of is the smallest integer such that for any radius and any node there is a
hitting setof size at most for all shortest paths that have an -witness path with .
This definition is stronger than the above, i.e., every graph of highway dimension also has highway dimension , but cannot be bounded in terms of .[5]
Shortest path cover
A notion closely related to the highway dimension is that of a shortest path cover,[1] where the order of the quantifiers in the definition is reversed, i.e., instead of a hub set for each ball, there is a one hub set , which is sparse in every ball:
Given a radius , an -shortest path cover of is a
hitting setfor all shortest paths in of length more than and at most . The -shortest path cover is locally -sparse if any node the ball contains at most vertices of , i.e., .
Every graph of bounded highway dimension (according to any of the above definitions) also has a locally -sparse -shortest path cover for every , but not vice versa.[4] For algorithmic purposes it is often more convenient to work with one hitting set for each radius , which makes shortest path covers an important tool for algorithms on graphs of bounded highway dimension.
Relation to other graph parameters
The highway dimension combines structural and metric properties of graphs, and is thus incomparable to common structural and metric parameters. In particular, for any graph it is possible to choose edge lengths such that the highway dimension is ,
Computing the highway dimension
Computing the highway dimension of a given graph is NP-hard.[5] Assuming that all shortest paths are unique (which can be done by slightly perturbing the edge lengths), an -approximation can be computed in polynomial time,[6] given that the highway dimension of the graph is . It is not known whether computing the highway dimension is
Algorithms exploiting the highway dimension
Shortest path algorithms
Some heuristics to compute shortest paths, such as the Reach, Contraction Hierarchies, Transit Nodes, and Hub Labelling algorithms, can be formally proven to run faster than other shortest path algorithms (e.g. Dijkstra's algorithm) on graphs of bounded highway dimension according to definition 3 above.[7]
Approximations for NP-hard problems
A crucial property that can be exploited algorithmically for graphs of bounded highway dimension is that vertices that are far from the hubs of a shortest path cover are clustered into so-called towns:[5]
Given a radius , an -shortest path cover of , and a vertex at distance more than from , the set of vertices at distance at most from according to the edge lengths is called a town. The set of all vertices not lying in any town is called the sprawl.
It can be shown that the diameter of every town is at most , while the distance between a town and any vertex outside it is more than . Furthermore, the distance from any vertex in the sprawl to some hub of is at most .
Based on this structure, Feldmann et al.[5] defined the towns decomposition, which recursively decomposes the sprawl into towns of exponentially growing values . For a graph of bounded highway dimension (according to definition 1 above) this decomposition can be used to find a
For clustering problems such as k-Median, k-Means, and Facility Location, faster polynomial-time approximation schemes (PTASs) are known for graphs of bounded highway dimension according to definition 1 above.[9] For network design problems such as TSP and Steiner Tree it is not known how to obtain a PTAS.
For the k-Center problem, it is not known whether a PTAS exists for graphs of bounded highway dimension, however it is NP-hard to compute a ()-approximation on graphs of highway dimension ,[10] which implies that any ()-approximation algorithm needs at least
For the Capacitated k-Center problem there is no PAS parameterized by and the highway dimension , unless
External links
- Video on "Capacitated k-Center in Low Doubling and Highway Dimension"[12] given by Tung Ahn Vu, 2022.
- Video on "Algorithms for Hard Problems on Low Highway Dimension Graphs" given by Andreas Emil Feldmann at ICERM, Brown University, Providence, US, May 2019.
- Video on "A (1 + ε)-Embedding of Low Highway Dimension Graphs into Bounded Treewidth Graphs"[5] given by Andreas Emil Feldmann at Hausdorff Institut, Bonn, DE, 2015.
- Video on "Highway Dimension: From Practice to Theory and Back"[6] given by Andrew Goldberg
References
- ^ S2CID 9330775.
- ISBN 978-1-61197-287-0, retrieved 2023-11-10
- ^ Bast, Holger; Funke, Stefan; Matijevic, Domagoj; Demetrescu, Camil; Goldberg, Andrew; Johnson, David (2006). "TRANSIT: Ultrafast Shortest-Path Queries with Linear-Time Preprocessing". The Shortest Path Problem: Ninth DIMACS Implementation Challenge.
- ^ S2CID 166228480.
- ^ S2CID 11339698.
- ^ ISBN 978-3-642-22006-7.
- ^ S2CID 1943037.
- .
- ISSN 0022-0000.
- ^ ISSN 1432-0541.
- .
- ^ ISBN 978-3-031-15914-5.