119 research outputs found
Defining Equitable Geographic Districts in Road Networks via Stable Matching
We introduce a novel method for defining geographic districts in road
networks using stable matching. In this approach, each geographic district is
defined in terms of a center, which identifies a location of interest, such as
a post office or polling place, and all other network vertices must be labeled
with the center to which they are associated. We focus on defining geographic
districts that are equitable, in that every district has the same number of
vertices and the assignment is stable in terms of geographic distance. That is,
there is no unassigned vertex-center pair such that both would prefer each
other over their current assignments. We solve this problem using a version of
the classic stable matching problem, called symmetric stable matching, in which
the preferences of the elements in both sets obey a certain symmetry. In our
case, we study a graph-based version of stable matching in which nodes are
stably matched to a subset of nodes denoted as centers, prioritized by their
shortest-path distances, so that each center is apportioned a certain number of
nodes. We show that, for a planar graph or road network with nodes and
centers, the problem can be solved in time, which improves
upon the runtime of using the classic Gale-Shapley stable matching
algorithm when is large. Finally, we provide experimental results on road
networks for these algorithms and a heuristic algorithm that performs better
than the Gale-Shapley algorithm for any range of values of .Comment: 9 pages, 4 figures, to appear in 25th ACM SIGSPATIAL International
Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL
2017) November 7-10, 2017, Redondo Beach, California, US
Analysis of Farthest Point Sampling for Approximating Geodesics in a Graph
A standard way to approximate the distance between any two vertices and
on a mesh is to compute, in the associated graph, a shortest path from
to that goes through one of sources, which are well-chosen vertices.
Precomputing the distance between each of the sources to all vertices of
the graph yields an efficient computation of approximate distances between any
two vertices. One standard method for choosing sources, which has been used
extensively and successfully for isometry-invariant surface processing, is the
so-called Farthest Point Sampling (FPS), which starts with a random vertex as
the first source, and iteratively selects the farthest vertex from the already
selected sources.
In this paper, we analyze the stretch factor of
approximate geodesics computed using FPS, which is the maximum, over all pairs
of distinct vertices, of their approximated distance over their geodesic
distance in the graph. We show that can be bounded in terms
of the minimal value of the stretch factor obtained using an
optimal placement of sources as , where is the ratio of the lengths of
the longest and the shortest edges of the graph. This provides some evidence
explaining why farthest point sampling has been used successfully for
isometry-invariant shape processing. Furthermore, we show that it is
NP-complete to find sources that minimize the stretch factor.Comment: 13 pages, 4 figure
A simpler and more efficient algorithm for the next-to-shortest path problem
Given an undirected graph with positive edge lengths and two
vertices and , the next-to-shortest path problem is to find an -path
which length is minimum amongst all -paths strictly longer than the
shortest path length. In this paper we show that the problem can be solved in
linear time if the distances from and to all other vertices are given.
Particularly our new algorithm runs in time for general
graphs, which improves the previous result of time for sparse
graphs, and takes only linear time for unweighted graphs, planar graphs, and
graphs with positive integer edge lengths.Comment: Partial result appeared in COCOA201
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