Discrete Dynamics in Nature and Society

Volume 2014 (2014), Article ID 160740, 4 pages

http://dx.doi.org/10.1155/2014/160740

## Topological Properties of a 3-Regular Small World Network

^{1}Department of Mathematics, Qinghai Normal University, Xining 810008, China^{2}Department of Computer Science, Qinghai Normal University, Xining 810008, China

Received 9 February 2014; Revised 11 April 2014; Accepted 12 April 2014; Published 29 April 2014

Academic Editor: Jinde Cao

Copyright © 2014 Huanshen Jia et al. 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

Complex networks have seen much interest from all research fields and have found many potential applications in a variety of areas including natural, social, biological, and engineering technology. The deterministic models for complex networks play an indispensable role in the field of network model. The construction of a network model in a deterministic way not only has important theoretical significance, but also has potential application value. In this paper, we present a class of 3-regular network model with small world phenomenon. We determine its relevant topological characteristics, such as diameter and clustering coefficient. We also give a calculation method of number of spanning trees in the 3-regular network and derive the number and entropy of spanning trees, respectively.

#### 1. Introduction

In recent years, research on complex networks is in the ascendant. Complex networks by using graph theory and some methods of statistical physics can be used to capture and describe the evolution of the system mechanism, evolution pattern, and the overall behavior, which is one of the main causes of vigorous development on complex networks research [1].

Construction of small-world networks that conform to the real system features not only has important theoretical significance but also has potential application value in deterministic way [2]. In this paper, we present 3-regular small-world network. We determine the relevant topological characteristics of the regular network, such as diameter and clustering coefficient. The results show that our model has a discrete exponential degree distribution, high clustering, and small diameter, which appears in a small-world effect. It is known that the number of spanning trees is an important quantity characterizing the reliability of a network. Because of the diverse applications in a number of fields, a lot of efforts have been devoted to the study of spanning trees. For example, in [3, 4] the author gave the accurate number of spanning trees in regular lattices. The number of spanning trees in a network can be obtained by directly calculating a related determinant corresponding to the network. We also give a method to calculate the number of spanning trees of the 3-regular network and derive the formulas. Based on the number of spanning trees we determine the entropy of its spanning trees.

#### 2. Network Construction

The degree of a vertex in is the number of edges of incident with , each loop counting as two edges [5]. A graph is -regular if for all . In this section we describe a model of growing network, which is constructed in an iterative manner. We denote our network after steps by . Then the network is constructed in the following way. For , is a graph that two nodes connect three edges. For , is obtained from by adding two new nodes at step ; then two edges are increased; replicate it and connect four new nodes. The process is repeated till the desired graph order is reached; see Figure 1.

Now we compute the size and order of . Denote by the total number of nodes in the network and by the total number of edges in the network. It is easy to get that Since and , it follows that Note that the addition of four new nodes leads to two new edges. Then As , we have

#### 3. Relevant Characteristics

Due to the determinacy, the relevant characteristics of our model described above can be solved exactly. In the following we concentrate on the diameter and clustering coefficient.

##### 3.1. Diameter

Small-world networks describe many real-life networks; that is, there is a relatively short distance between most pairs of nodes in most real-life networks and their average path length (APL) does not increase linearly with the system size but grows logarithmically with the number of nodes or slower. The average path length is the smallest number of links connecting a pair of nodes, averaged over all pairs of nodes [2]. The longest shortest path between all pairs of nodes is called diameter, which is one of the most important evaluation index because it characterizes the maximum communication delay in the network [6]. For most network models, it is hard to obtain the analytic solution of the average path length. So we will study the diameter instead of average path length. We denote the diameter at iteration as ; then according to Figure 1, we can clearly know that and . At each iteration for , one can see that the diameter always lies between a pair of newly created nodes at this iteration. Thus the diameter for the network proposed has the following formula:

So, the diameter grows logarithmically with the number of nodes. Because the average path length is smaller than diameter, the APL should increase more slowly. So our model satisfies the property for small-world networks.

##### 3.2. Clustering Coefficient

Clustering is another important property of a complex network, which provides a measure of the local structure within the network [2]. The clustering coefficient indicates the connection relationship between a node and its neighborhood nodes. By definition, clustering coefficient of a node with degree is the ratio of the total number of existing edges between all its nearest neighbors and the number of all possible edges between them; that is, . The clustering coefficient of the whole network is the average of all individual ; that is, , where is the number of nodes of the network. So the clustering coefficient for our network has the following simple formula. We know at step . At each iteration , we have Obviously, when ; thus the clustering is high and also satisfies the properties for small-world networks.

Based on the above discussion, we can conclude that our model is a deterministic small-world network, because it is sparse with small diameter and average path length and high clustering coefficient.

#### 4. Enumeration of Spanning Trees

A spanning tree of any connected network is defined as a minimal set of edges that connect every node. The enumeration of spanning trees in networks is a fundamental issue in mathematics [7–9], physics [3, 10], and other disciplines [11]. The problem of spanning trees is relevant to various aspects of networks, such as reliability [12, 13], optimal synchronization [14], and random walks [15]. In what follows we will examine the number of spanning trees in the 3-regular network.

In order to calculate the number of spanning trees accurately, at first we build a new model shown in Figure 2. We list the relationship between and in Figure 3. The number of the spanning trees and the spanning forests with two components such as and belongs to distinct components of that are denoted by and , respectively. Then the number of spanning trees of the original model can be obtained from the number of the spanning trees of . Then we have the following relationship:

We calculate the number of spanning trees of in the following. It is easy to obtain the relationship of and from (7). From Figure 4, one can see that the shape of the spanning trees of includes two cases.

And the shape of the spanning forests with two components such as and belongs to distinct components of that have five cases; see Figure 5.

By considering the symmetric, it is not hard to get the recurrence relation of the number of spanning trees and spanning forests of and in Figures 4 and 5: with the initial conditions and . By computation, it follows that Let ; then with . So .

Noticing that , by (8), we know that

Note that is the number of nodes of . From (10) we determine the entropy of the spanning trees of [7, 16] as follows: From (10) and (11), together with (7), the number of spanning trees of is given by with the initial condition .

So, the entropy of the spanning trees of is given by

We can compare the entropy of the spanning trees of with other networks [17, 18]. The entropy of the spanning trees in Koch networks with an average degree of 3 is 0.5493 [17], while the entropy of the spanning trees of our model is 0.6931.

#### 5. Conclusions

In this paper, we proposed a class of deterministic regular small-world model which is constructed in an iterative manner and presented an exhaustive analysis of many properties of considered model. Then we obtained the analytic solutions for most of the topological features, including diameter and clustering coefficient. We also determined the number of spanning trees in the 3-regular small-world network. In addition, using the algorithm, we obtained the entropies of spanning trees.

#### Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

#### Acknowledgments

This research is supported by the National Science Foundation of China (nos. 61164005 and 60863006), the National Basic Research Program of China (no. 2010CB334708), and Program for Changjiang Scholars and Innovative Research Team in University (no. IRT1068).

#### References

- R. Albert and A.-L. Barabási, “Statistical mechanics of complex networks,”
*Reviews of Modern Physics*, vol. 74, no. 1, pp. 47–97, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus - Z.-Z. Zhang, L.-L. Rong, and F. Comellas, “Evolving small-world networks with geographical attachment preference,”
*Journal of Physics A: Mathematical and General*, vol. 39, no. 13, pp. 3253–3261, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus - F. Y. Wu, “Number of spanning trees on a lattice,”
*Journal of Physics A: General Physics*, vol. 10, no. 6, Article ID L113, 1977. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - R. Shrock and F. Y. Wu, “Spanning trees on graphs and lattices in d dimensions,”
*Journal of Physics A: Mathematical and General*, vol. 33, no. 21, pp. 3881–3902, 2000. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus - J. A. Bondy and U. S. R. Murty,
*Graph Theory with Applications*, American Elsevier, New York, NY, USA, 1976. View at MathSciNet - Z.-M. Lu and S.-Z. Guo, “A small-world network derived from the deterministic uniform recursive tree,”
*Physica A: Statistical Mechanics and its Applications*, vol. 391, no. 1-2, pp. 87–92, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - R. Burton and R. Pemantle, “Local characteristics, entropy and limit theorems for spanning trees and domino tilings via transfer-impedances,”
*The Annals of Probability*, vol. 21, no. 3, pp. 1329–1371, 1993. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - R. Lyons, “Asymptotic enumeration of spanning trees,”
*Combinatorics Probability and Computing*, vol. 14, no. 4, pp. 491–522, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus - R. Lyons, R. Peled, and O. Schramm, “Growth of the number of spanning trees of the Erdős-Rényi giant component,”
*Combinatorics Probability and Computing*, vol. 17, no. 5, pp. 711–726, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus - S.-C. Chang, L.-C. Chen, and W.-S. Yang, “Spanning trees on the Sierpinski gasket,”
*Journal of Statistical Physics*, vol. 126, no. 3, pp. 649–667, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - R. Jayakumar, K. Thulasiraman, and M. N. S. Swamy, “MOD-CHAR: an implementation of Char's spanning tree enumeration algorithm and its complexity analysis,”
*IEEE Transactions on Circuits and Systems*, vol. 36, no. 2, pp. 219–228, 1989. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - F. T. Boesch, “On unreliability polynomials and graph connectivity in reliable network synthesis,”
*Journal of Graph Theory*, vol. 10, no. 3, pp. 339–352, 1986. View at Publisher · View at Google Scholar · View at MathSciNet - G. J. Szabó, M. Alava, and J. Kertész, “Geometry of minimum spanning trees on scale-free networks,”
*Physica A: Statistical Mechanics and its Applications*, vol. 330, no. 1-2, pp. 31–36, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - T. Nishikawa and A. E. Motter, “Synchronization is optimal in nondiagonalizable networks,”
*Physical Review E: Statistical, Nonlinear, and Soft Matter Physics*, vol. 73, no. 6, Article ID 065106, 2006. View at Publisher · View at Google Scholar · View at Scopus - D. Dhar and A. Dhar, “Distribution of sizes of erased loops for loop-erased random walks,”
*Physical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics*, vol. 55, no. 3, pp. R2093–R2096, 1997. View at Google Scholar · View at Scopus - R. Lyons, “Asymptotic enumeration of spanning trees,”
*Combinatorics Probability and Computing*, vol. 14, no. 4, pp. 491–522, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus - Z. Z. Zhang, S. Y. Gao, L. C. Chen, S. G. Zhou, H. J. Zhang, and J. H. Guan, “Mapping Koch curves into scale-free small-world networks,”
*Journal of Physics A: Mathematical and Theoretical*, vol. 43, no. 39, Article ID 395101, 2010. View at Publisher · View at Google Scholar - Z. Z. Zhang, H. X. Liu, B. Wu, and S. G. Zhou, “Enumeration of spanning trees in a pseudofractal scale-free web,”
*Europhysics Letters*, vol. 90, no. 6, Article ID 680029, 2010. View at Google Scholar