Title
Learning random points from geometric graphs or orderings
Abstract
Let X-v for v is an element of V be a family of n iid uniform points in the square <SIC>& xdcae;n=-n/2,n/22. Suppose first that we are given the random geometric graph G is an element of G(n,r), where vertices u and v are adjacent when the Euclidean distance d(E)(X-u,X-v) is at most r. Let n(3/14)MUCH LESS-THANrMUCH LESS-THANn(1/2). Given G (without geometric information), in polynomial time we can with high probability approximately reconstruct the hidden embedding, in the sense that "up to symmetries," for each vertex v we find a point within distance about r of X-v; that is, we find an embedding with "displacement" at most about r. Now suppose that, instead of G we are given, for each vertex v, the ordering of the other vertices by increasing Euclidean distance from v. Then, with high probability, in polynomial time we can find an embedding with displacement O(logn).
Year
DOI
Venue
2018
10.1002/rsa.20922
RANDOM STRUCTURES & ALGORITHMS
Keywords
Field
DocType
approximate embedding,random geometric graphs,unit disk graphs,vertex orders
Binary logarithm,Graph,Combinatorics,Embedding,Vertex (geometry),Euclidean distance,Time complexity,Random geometric graph,Mathematics,Homogeneous space
Journal
Volume
Issue
ISSN
57.0
2.0
1042-9832
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Josep Díaz1489204.59
Colin McDiarmid21071167.05
Dieter Mitsche314126.08