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Sharpness in the k-Nearest-Neighbours Random Geometric Graph Model

Published online by Cambridge University Press:  04 January 2016

Victor Falgas-Ravry*
Affiliation:
Queen Mary, University of London
Mark Walters*
Affiliation:
Queen Mary, University of London
*
Postal address: School of Mathematical Sciences, Queen Mary, University of London, London E1 4NS, UK.
Postal address: School of Mathematical Sciences, Queen Mary, University of London, London E1 4NS, UK.
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Abstract

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Let Sn, k denote the random graph obtained by placing points in a square box of area n according to a Poisson process of intensity 1 and joining each point to its k nearest neighbours. Balister, Bollobás, Sarkar and Walters (2005) conjectured that, for every 0 < ε < 1 and all sufficiently large n, there exists C = C(ε) such that, whenever the probability that Sn, k is connected is at least ε, then the probability that Sn, k+C is connected is at least 1 - ε. In this paper we prove this conjecture. As a corollary, we prove that there exists a constant C' such that, whenever k(n) is a sequence of integers such that the probability Sn,k(n) is connected tends to 1 as n → ∞, then, for any integer sequence s(n) with s(n) = o(logn), the probability Sn,k(n)+⌊C'slog logn is s-connected (i.e. remains connected after the deletion of any s − 1 vertices) tends to 1 as n → ∞. This proves another conjecture given in Balister, Bollobás, Sarkar and Walters (2009).

Type
Stochastic Geometry and Statistical Applications
Copyright
© Applied Probability Trust 

References

Balister, P., Bollobás, B., Sarkar, A. and Walters, M. (2005). Connectivity of random k-nearest-neighbour graphs. Adv. Appl. Prob. 37, 124.Google Scholar
Balister, P., Bollobás, B., Sarkar, A. and Walters, M. (2009). A critical constant for the k-nearest-neighbour model. Adv. Appl. Prob. 41, 112.Google Scholar
Balister, P., Bollobás, B., Sarkar, A. and Walters, M. (2009). Highly connected random geometric graphs. Discrete Appl. Math. 157, 309320.CrossRefGoogle Scholar
Gilbert, E. N. (1961). Random plane networks. J. Soc. Indust. Appl. Math. 9, 533543.Google Scholar
González-Barrios, J. M. and Quiroz, A. J. (2003). A clustering procedure based on the comparison between the k nearest neighbors graph and the minimal spanning tree. Statist. Prob. Lett. 62, 2334.CrossRefGoogle Scholar
Penrose, M. D. (1997). The longest edge of the random minimal spanning tree. Ann. Appl. Prob. 7, 340361.Google Scholar
Penrose, M. D. (1999). On k-connectivity for a geometric random graph. Random Structures Algorithms 15, 145164.Google Scholar
Penrose, M. (2003). Random Geometric Graphs (Oxford Stud. Prob. 5). Oxford University Press.Google Scholar
Walters, M. (2012). Small components in k-nearest neighbour graphs. Discrete Appl. Math. 160, 20372047.Google Scholar
Xue, F. and Kumar, P. R. (2004). The number of neighbors needed for connectivity of wireless networks. Wireless Networks 10, 169181.CrossRefGoogle Scholar