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On the size of a random sphere of influence graph

Part of: Stochastic processes Geometric probability and stochastic geometry Graph theory Combinatorial probability

Published online by Cambridge University Press:  01 July 2016

T. K. Chalker ,
A. P. Godbole ,
P. Hitczenko ,
J. Radcliff  and
O. G. Ruehr
T. K. Chalker*
Affiliation:
University of California, Los Angeles
A. P. Godbole*
Affiliation:
Michigan Technological University
P. Hitczenko*
Affiliation:
North Carolina State University
J. Radcliff*
Affiliation:
University of Michigan, Ann Arbor
O. G. Ruehr*
Affiliation:
Michigan Technological University
*
∗ Postal address: Department of Mathematics, University of California, Los Angeles, CA 90024, USA.
∗∗ Email address: anant@mtu.edu
∗∗∗ Postal address: Department of Mathematics, North Carolina State University, Raleigh, NC 27607, USA.
∗∗∗∗ Postal address: Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA.
∗∗∗∗∗ Postal address: Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA.
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Abstract

We approach sphere of influence graphs (SIGs) from a probabilistic perspective. Ordinary SIGs were first introduced by Toussaint as a type of proximity graph for use in pattern recognition, computer vision and other low-level vision tasks. A random sphere of influence graph (RSIG) is constructed as follows. Consider n points uniformly and independently distributed within the unit square in d dimensions. Around each point, Xi, draw an open ball (‘sphere of influence’) with radius equal to the distance to Xi's nearest neighbour. Finally, draw an edge between two points if their spheres of influence intersect. Asymptotically exact values for the expected number of edges in a RSIG are determined for all values of d; previously, just upper and lower bounds were known for this quantity. A modification of the Azuma-Hoeffding exponential inequality is employed to exhibit the sharp concentration of the number of edges around its expected value.

Keywords

Azuma-Hoeffding inequalityrandom graphssphere of influence graphs

MSC classification

Primary: 60D05: Geometric probability and stochastic geometry
Secondary: 60C05: Combinatorial probability 60G42: Martingales with discrete parameter 05C80: Random graphs
Type
Stochastic Geometry and Statistical Applications
Information
Advances in Applied Probability , Volume 31 , Issue 3 , September 1999 , pp. 596 - 609
Copyright
Copyright © Applied Probability Trust 1999 

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