ISRN Discrete MathematicsVolume 2012 (2012), Article ID 758721, 6 pagesdoi:10.5402/2012/758721

Research Article

Uniqueness of the Infinite Component for Percolation on a Hierarchical Lattice

Yilun Shang

Institute for Cyber Security, University of Texas at San Antonio, San Antonio, TX 78249, USA

Received 5 July 2012; Accepted 16 August 2012

Academic Editors: U. A. Rozikov and X. Yong

Copyright © 2012 Yilun Shang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

We study a long-range percolation in the hierarchical lattice of order where probability of connection between two nodes separated by distance is of the form min, and . We show the uniqueness of the infinite component for this model.

1. Introduction

Percolation theory in the Euclidean lattice started with the work of Broadbent and Hammersley in 1957. The infinity of the space of sites (or vertices) and its geometry are principal features of this model, see for example [1, 2]. Some questions of percolation in other non-Euclidean infinite systems are formulated in [3]. The study of long-range percolation on traces back to [4] and leads to a range of interesting results in probability theory and statistical physics [5–9]. On the other hand, hierarchical structures have been used in applications in the physics, genetics, and social sciences thanks to the multiscale organization of many natural objects [10–13].

Recently, long-range percolation is studied on the hierarchical lattice of order (to be defined below), where classical methods for the usual lattice break down. The asymptotic long-range percolation on is addressed in [14] for . The works [15–17], analyze the phase transition of long-range percolation on for finite using different connection probabilities and methodologies. The contact process on for fixed has been investigated in [18]. In this paper, we investigate the question of uniqueness of infinite component in percolation on for fixed . The form of the connection probabilities used here follows from a prior work [17].

For an integer , we define the set and define a metric on it: The pair is called the hierarchical lattice of order , which may be thought of as the set of leaves at the bottom of an infinite regular tree without a root, where the distance between two nodes is the number of levels (generations) from the bottom to their most recent common ancestor. Figure 1 shows the lattice along with its metric generating tree.

Figure 1: An illustration of hierarchical lattice of order 2. The distances between three nodes , and are and .

Such a distance satisfies the strong triangle inequality for any triple . Hence, is an ultrametric (or non-Archimedean) space [19]. From its ultrametricity, it is clear that for every that there are nodes at distance from it.

Now consider a long-range percolation on . For each , the probability of connection between and such that is given by where and , all connections being independent. Two vertices are in the same component if there exists a finite sequence of vertices such that each pair , , of vertices presents an edge.

The rest of the paper is organized as follows. In Section 2, we provide the uniqueness result and Section 3 is devoted to the proof.

2. Main results

Let be the nonnegative integers including 0, and denote by . Let be the size of a set . The connected component containing the node is denoted by . Since, for every node , has the same distribution, it suffices to consider only . The percolation probability is defined as and the critical percolation value is defined as The following theorem characterizes the phase transition for this model.

Theorem 2.1 (see [17]). (i) If , then . (ii) If , then .(iii) If , then .

The uniqueness of infinite component is established in the following result.

Theorem 2.2. For and , there is at most one infinite component almost surely.

3. Proof of Theorem 2.2

For any node , define the ball of radius around , that is, . From this definition, we make the following observations. Firstly, for any , contains vertices. Secondly, if . Finally, for any , , and , we either have or .

The proof of Theorem 2.2 follows the idea in [16, Theorem 1.2] and is based on several lemmas.

Lemma 3.1 (see [20]). Consider long range percolation on with the properties (i)the model is translation-invariant(ii)the model satisfies the positive finite energy condition. Then there can be at most one infinite component almost surely.

Lemma 3.2. The original metric generating tree (as shown in Figure 1) can be embedded into in a stationary way.

Proof. We will prove this lemma in two steps. (i)Construct a new metric generating tree, which is isomorphic to the original metric generating tree.(ii)The new metric generating tree is stationary on . To show step (i), we first describe the construction roughly and then provide the formal construction. The new metric generating tree embeds into in such a way that for every , (a) any ball of radius will be represented by consecutive integers and (b) the collection of balls of radius partitions .We choose uniformly at random among all possible collections of consecutive integers containing the origin 0 in . Given the choice of , all other balls of radius 1 can be defined, although not specified at this point, by the above criteria (a) and (b). Next, the ball is a union of balls of radius 1 and contains . Since any ball of radius 2 is a collection of consecutive integers, there are possible ways to achieve this. We choose one of the possible ways to do this with probability each. Once we have chosen , all other balls of radius 2 are determined for the same reason as above. We continue this procedure to obtain the new metric generating tree, which is embedded in . To get a picture of this, we illustrate in Figure 2 a possible implementation for .Now we formalize the above construction. We choose the probability space as the unit interval with Borel sigma field and Lebesgue measure. For , denote by the -adic expansion. In other words, where we assume that the expansion for is unique without loss of generality. In the above construction, for each , is one of the balls of radius among the balls making up . The new metric generating tree corresponding to is obtained as follows. We let be such that is the -st ball in from left to right. In Figure 2, we can see, for example, and . It is clear that this construction formalize the informal description given earlier. By first identifying the in Figure 1 and in Figure 2, and then building up the balls for , in that order, we can see that the new metric generating tree is isomorphic to the original one.Next, we move to step (ii). Let be the map that assigns to each a new metric generating tree as before. The map is invertible on a set of complete Lebesgue measure. Denote by the left-shift transformation, which translates the edges over one unit to the left on the new metric generating trees. Let the transformation correspond to the left-shift transformation on the space of new metric generating trees in the sense that (see Figure 3), hence . Let , and then we can see that the th digit in , , is given by Furthermore, Lebesgue measure is invariant under the action of , which implies that the construction of new random metric generating tree is stationary on .

Figure 2: A realization of a new metric generating tree for . corresponds to , corresponds to , and corresponds to .

Figure 3: The commutative diagram.

Now we are at the stage to prove Theorem 2.2.

Proof of Theorem 2.2. We assign a uniformly- distributed random variable to each edge in such a way that the collection is independent. Given a new metric generating tree, an edge is said to be open if , where denotes the length of . This gives a realization of the percolation process with the correct distribution and shows that the whole long-rang percolation process on the hierarchical lattice can be embedded as a stationary percolation process on involving Lemma 3.2.For any and , every pair of vertices are connected by an edge with positive probability, irrespective of the presence or absence of other edges. Therefore, the positive finite energy condition is satisfied. The result then follows from Lemma 3.1. As for , the result is immediate.

To conclude the paper, we mention that the uniqueness of infinite component has also been proved in [15, 16] for connection probabilities with and , respectively.

Acknowledgment

The author would like to thank the referees who provided valuable suggestions and helpful comments.

References

B. Bollobás and O. Riordan, Percolation, Cambridge University Press, New York, NY, USA, 2006.

G. Grimmett, Percolation, vol. 321, Springer, Berlin, Germany, 2nd edition, 1999.

I. Benjamini and O. Schramm, “Percolation beyond , many questions and a few answers,” Electronic Communications in Probability, vol. 1, pp. 71–82, 1996.

Z. Q. Zhang, F. C. Pu, and B. Z. Li, “Long-range percolation in one dimension,” Journal of Physics. A, vol. 16, no. 3, pp. L85–L89, 1983.

M. Aizenman and C. M. Newman, “Discontinuity of the percolation density in one-dimensional percolation models,” Communications in Mathematical Physics, vol. 107, no. 4, pp. 611–647, 1986.

N. Berger, “Transience, recurrence and critical behavior for long-range percolation,” Communications in Mathematical Physics, vol. 226, no. 3, pp. 531–558, 2002.

M. Biskup, “On the scaling of the chemical distance in long-range percolation models,” The Annals of Probability, vol. 32, no. 4, pp. 2938–2977, 2004.

D. Coppersmith, D. Gamarnik, and M. Sviridenko, “The diameter of a long-range percolation graph,” Random Structures & Algorithms, vol. 21, no. 1, pp. 1–13, 2002.

P. Trapman, “The growth of the infinite long-range percolation cluster,” The Annals of Probability, vol. 38, no. 4, pp. 1583–1608, 2010.

A. L. Barabási and E. Ravaz, “Hierarchical organization in complex networks,” Physical Review E, vol. 67, Article ID 026112, 2003.

R. Rammal, G. Toulouse, and M. A. Virasoro, “Ultrametricity for physicists,” Reviews of Modern Physics, vol. 58, no. 3, pp. 765–788, 1986.

Y. Shang, “Leader-following consensus problems with a time-varying leader under measurement noises,” Advances in Dynamical Systems and Applications, vol. 6, pp. 255–270, 2011.

J. Shen, “Cucker-Smale flocking under hierarchical leadership,” SIAM Journal on Applied Mathematics, vol. 68, no. 3, pp. 694–719, 2007/08.

D. A. Dawson and L. G. Gorostiza, “Percolation in a hierarchical random graph,” Communications on Stochastic Analysis, vol. 1, no. 1, pp. 29–47, 2007.

D. A. Dawson and L. G. Gorostiza, “Percolation in an ultrametric space,” submitted, http://arxiv.org/abs/1006.4400

V. Koval, R. Meester, and P. Trapman, “Long-range percolation on the hierarchical lattice,” Electronic Journal of Probability, vol. 17, no. 57, 2012.

Y. Shang, “Percolation in a hierarchical lattice,” Zeitschrift für Naturforschung, vol. 67a, no. 5, pp. 225–229, 2012.

S. R. Athreya and J. M. Swart, “Survival of contact processes on the hierarchical group,” Probability Theory and Related Fields, vol. 147, no. 3-4, pp. 529–563, 2010.

P. Schneider, Nonarchimedean Functional Analysis, Springer, Berlin, Germany, 2002.

A. Gandolfi, M. S. Keane, and C. M. Newman, “Uniqueness of the infinite component in a random graph with applications to percolation and spin glasses,” Probability Theory and Related Fields, vol. 92, no. 4, pp. 511–527, 1992.