Connected Spatial Networks over Random Points and a Route-Length Statistic

被引:46
|
作者
Aldous, David J. [1 ]
Shun, Julian [2 ]
机构
[1] Univ Calif Berkeley, Dept Stat, Berkeley, CA 94720 USA
[2] Carnegie Mellon Univ, Machine Learning Dept, Pittsburgh, PA 15213 USA
关键词
Proximity graph; random graph; spatial network; geometric graph; MINIMAL SPANNING-TREES; COMPUTATIONAL GEOMETRY; GABRIEL GRAPHS;
D O I
10.1214/10-STS335
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We review mathematically tractable models for connected networks on random points in the plane, emphasizing the class of proximity graphs which deserves to be better known to applied probabilists and statisticians. We introduce and motivate a particular statistic R measuring shortness of routes in a network. We illustrate, via Monte Carlo in part, the trade-off between normalized network length and R in a one-parameter family of proximity graphs. How close this family comes to the optimal trade-off over all possible networks remains an intriguing open question. The paper is a write-up of a talk developed by the first author during 2007-2009.
引用
收藏
页码:275 / 288
页数:14
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