Scale-free networks without growth

Scale-free networks without growth

Physica A 387 (2008) 1683–1688 www.elsevier.com/locate/physa Scale-free networks without growth Yan-Bo Xie, Tao Zhou ∗ , Bing-Hong Wang Department of...

341KB Sizes 0 Downloads 5 Views

Physica A 387 (2008) 1683–1688 www.elsevier.com/locate/physa

Scale-free networks without growth Yan-Bo Xie, Tao Zhou ∗ , Bing-Hong Wang Department of Modern Physics and Nonlinear Science Centre, University of Science and Technology of China, Hefei, 230026, PR China Received 22 July 2007; received in revised form 17 October 2007 Available online 12 November 2007

Abstract In this paper, we proposed an ungrowing scale-free network model, indicating the growth may not be a necessary condition of the self-organization of a network in a scale-free structure. The analysis shows that the degree distributions of the present model can varying from the Poisson form to the power-law form with the decrease of a free parameter α. This model provides a possible mechanism for the evolution of some scale-free networks with fixed size, such as the friendship networks of school children and the functional networks of the human brain. c 2007 Elsevier B.V. All rights reserved.

PACS: 89.75.Hc; 64.60.Ak; 84.35.+i; 05.40.-a Keywords: Complex networks; Modelling; Scale-free network

Many social, biological and communication systems can be properly described as complex networks with nodes representing individuals and edges mimicking the interactions among them [1–3]. Examples are numerous: these include the Internet [4], the World Wide Web [5], social networks [6], opinion networks [7], metabolic networks [8], food webs [9] and many others. Recent empirical studies indicate that those networks in various fields have some common characteristics [10], the most important of which are called the small-world effect [11] and the scale-free property [12]. The ubiquity of complex networks and the discoveries of the common network properties inspire scientists to construct general models. In the simplest way, these models can be categorized into two classes, the growing models and the ungrowing ones. In the growing models, the number of nodes in network grows continuously, while in ungrowing cases, the number of nodes is fixed. The Watts–Strogatz (WS) model is the pioneer and representative one of ungrowing models, which can be constructed by starting with a regular network and randomly moving one endpoint of each edge with probability p [11]. WS networks display small-world effect, that is, they are highly clustered and of small average distance. The most successful one of growing models is the Barab´asi–Albert (BA) model [12], which suggests that two main ingredients of the self-organization of a network in a scale-free structure are growth and preferential attachment. These points to the facts that most networks grow continuously by adding new nodes, which are preferentially attached to existing nodes with a large number of neighbours. Thus far, various proposals for dynamical evolution of scale-free networks has been introduced. Roughly, these models can be classified into three main scenarios. One is under the preferential attachment mechanism [12–18], and a related

∗ Corresponding author.

E-mail address: [email protected] (T. Zhou). c 2007 Elsevier B.V. All rights reserved. 0378-4371/$ - see front matter doi:10.1016/j.physa.2007.11.005

1684

Y.-B. Xie et al. / Physica A 387 (2008) 1683–1688

scenario is found in the protein duplication model [19]. The second one is a balance between a modelled tendency to form hubs against an entropy pressure towards a random networks [20–22]. The third one is the emergence of scale-free network by some self-organized evolving process [23]. By using the mean-field theory (see two typical works on mean-field theory about network evolving process [13, 14]) on a toy model, Barab´asi et al. assert that the growth is one of the necessary conditions for the emergence of scalefree property [13], and the networks without growth may display an exponential degree distribution. This hypothesis is now widely accepted [1,12,13]. However, some real-life networks having fixed size (or almost fixed size) are of scalefree property. For instance, consider the friendship networks of school children wherein the students are represented by nodes and two nodes are connected by an edge if the corresponding students have good friendship [24,25]. These networks do not grow with time since the number of students in a class is almost fixed. Some students have many good friends, while many others have only a few friends. The students’ heterogeneous societal ability leads to a skewed degree distribution (or a skewed strength distribution for weighted case [25]), approximated to a power-law form. Another typical example is the functional networks of human brain [26]. Although its structure varies with time, the brain functional network’s sizes is unchanged [27] while it displays scale-free property. Although the two kinds of networks mentioned above have fixed size, they are not static since their structures vary with time. These motions can be considered as rewiring processes, that is, some existing edges are removed while some new ones are added. For example, in friendship networks, good friends may quarrel about beliefs, money, or some other things, and become impassive to each other; while some ones will become good friends for their common interests and difficulties. Notice that, in the ungrowing model proposed by Barab´asi et al. [13], no edges will be removed thus no rewiring processes will occur. Although a few works about network rewiring models are previously reported [28–31], compared with the extensive exploration of growing network models, the systematic studies of ungrowing models are lacking. In this paper, an alterative scale-free network model without growth is proposed, and we argue that the ungrowing networks can have power-law degree distributions, which attributes to the rewiring processes. For simplicity, we consider a network model with both fixed number of nodes N and fixed number of edges E. The initial state of the network can be a random graph or any other types of graph. Then the network evolves based on the following rewiring processes: At each time step, an edge is randomly selected and removed from the network. At the same time, a node is selected with the preferential probability ki + α Π (ki ) = P , (k j + α)

(1)

j

where ki is the degree of the ith node, and α > 0 is a constant representing the original attraction of each node [16]. Another node is selected also with the above preferential probability. Then a new edge connecting these two nodes is created. Notice that in this rewiring process, the total number of edges is unchanged. The above process is repeated at each time step. Finally, we expect that the network reaches an equilibrium state which is independent of the initial state. Let P(k, t) represents the number of nodes with degree k at the time step t. P(k, t) satisfies the following normalization conditions X P(k, t) = N , (2) k

X

k P(k, t) = 2E.

(3)

k

It is straightforward to write down the master equation for P(k, t), P(k, t + 1) − P(k, t) =

(k + 1)P(k + 1, t) − k P(k, t) E 2 + [(k − 1 + α)P(k − 1, t) − (k + α)P(k, t)]. 2E + N α

(4)

The validity of Eq. (4) is restricted in the interval E ≥ k > 0, and for ∀k > E, we set P(k, t) = 0. It may be helpful to explain the physical meaning of various terms in the above equation. The first two terms on the right hand

Y.-B. Xie et al. / Physica A 387 (2008) 1683–1688

1685

side represent the net gain of P(k, t) due to the edge removing process. In particular, the first term represents the net gain of P(k, t) when the removing edge connects a node with the degree k + 1. Notice that when an edge is randomly selected, the connectingP nodes have more chance to be of large degree. Explicitly, the node with degree k is selected with the probability k/ k k P(k, t) = k/2E. Since each edge connects to two nodes, the first term is thus (k + 1)P(k + 1, t)/E. Similarly, the second term represents the net loss of P(k, t) when the removing edge connects a node with the degree k. The third and fourth terms represents the net gain of P(k, t) due to the edge adding process. The third term represents the net gain of P(k, t) when the adding edge P connects a node with the degree k − 1. Notice that a node with degree k is selected with the probability (k + α)/ k (k + α)P(k, t) = (k + α)/(2E + α N ). The fourth term represents the net loss of P(k, t) when the adding edge connects a node with the degree k. When t is sufficiently large, we expect that P(k, t) approaches a stationary state denoted by Ps (k) that satisfies Ps (k + 1) =

1 [(k(2γ + α) + γ α)Ps (k) − γ (k − 1 + α)Ps (k − 1)] (k + 1)(γ + α)

(5)

with γ = 2E/N representing the density of connectivity. Given Ps (0) and Ps (1), one can obtain Ps (k) for k > 1 based on the above iteration equation. The values of Ps (0) and Ps (1) are determined by the normalization conditions Eqs. (2) and (3). Although two initial conditions Ps (0) and Ps (1) are required, fortunately, the analytical solutions of Eq. (5) are obtained. In the continuous limit, Eq. (5) corresponds to a second order differential equation. It is not difficult to show that the following expression  γ α (6) Ps (0) = N α+γ  α  k α γ 1 Ps (k) = N α(α + 1) · · · (α + k − 1), k ≥ 1 (7) α+γ α+γ k! satisfying Eq. (5) and the normalization conditions Eqs. (2) and (3) (Eq. (7) is the exact solution of Eq. (5)). Note that, the solution Eq. (7) has unphysical part, that is, for k > E, Ps (k) is bigger than zero. This unphysical part can be ignored only if there is essentially no one node has finite fraction of the edges in the large E limit (which is also called the “safe situations” in Ref. [32]). When α  1, the mechanism of preferential attachment is destroyed, that is, since α probably plays the main part in the sum α + ki , the probability a high degree node being selected is approximately the same as that of a low degree node (see Eq. (1)). Therefore, the selection mechanism is completely random and one could expect that the present network will approach a completely random network with its degree distribution obeying the Poisson distribution [33] e−γ k γ . (8) k! Note that, Eq. (8) can also be obtained directly by considering the large α limit in Eq. (7). In the α → ∞ limit, α can be rewritten as 1 − γα , therefore (α + γ )k ≈ α(α + 1) · · · (α + k − 1), and α+γ Ps (k) = N

 γ α k e−γ k Ps (k)α→∞ = lim N 1 − γ . (9) γ =N α→∞ α k! When α  1 and γ  1, most nodes are isolated and the nodes with large degree are very few. However, when α  1 and γ  1, interesting result emerges. The corresponding degree distribution obeys a power-law form with exponent approximated to one when k  γ /α  α α α Ps (k) = N . (10) γ k In succession, we will show some simulations. 106 time steps are performed in all simulations while only the final 2 × 105 time steps are used for statistical average. The initial graph is simply the completely random graph with given N and E. The network size if fixed as N = 1000 unless a special statement is addressed. In Fig. 1, we report the case for very large α, where ps (k) = Ps (k)/N denotes the normalized probability function for degree

1686

Y.-B. Xie et al. / Physica A 387 (2008) 1683–1688

Fig. 1. (Colour online) The degree distribution of the present model for the case α  1. The black hollow squares and red solid circles represent the analytical and simulation results, respectively. The analytical result agrees with the simulation accurately, and both of them obey Poisson forms.

Fig. 2. The degree distribution of the present model with α = 0.01 and γ = 5. The hollow squares and solid curve represent the simulation and analytical results, respectively. The analytical result agrees with the simulation well, obeying an approximately power-law form. The inset exhibits the case of α = 0.01 and γ = 10 for comparison.

distribution. The simulation result strongly supports the analytic one (see Eq. (8)), and both of them obey Poisson forms. In this figure, one will find that there are about 5% nodes with degree zero. However, this probability only reflects the average number of isolated nodes over all configurations. Because of the rewiring mechanism, no nodes are always isolated. Although real-life networks are not always connected, most previous studies have focused on connected graphs. Therefore, hereinafter, we neglect the isolated nodes. In Fig. 2, we report the analytical and simulation results about degree distributions generated by very small α = 0.01 and γ = 5. The degree distribution displays obviously scale-free property with a cut-off at k ≈ 120. The solid curve denotes the analytical solution, which agrees with the simulation well. We also have checked that the departure from analytical solution will get greater if the parameter γ becomes larger, and the scale-free property will vanish when γ  1. In the inset, the case of γ = 10 is shown for comparison. As mentioned above, the two extreme cases with α ≈ 0 and α  1 will lead to the power-law and Poisson distributions, respectively. In Fig. 3, we report the simulation results for α = 0.1 and α = 1.0. The departure from the power law becomes larger as the increase of α, and the degree distribution with α = 1.0 obeys approximately an exponential form rather than a power-law form. In conclusion, we proposed an ungrowing model, which can generate networks with different classes of degree distributions by tuning two parameters. Especially, when α is close to zero and γ  1 (the symbol “” means bigger than but not very much bigger than), the present networks display scale-free property. The previous empirical studies

Y.-B. Xie et al. / Physica A 387 (2008) 1683–1688

1687

Fig. 3. (Colour online) The degree distributions of the present model. The parameter γ = 10 is fixed, and the black squares and red circles represent the cases with α = 1.0 and α = 0.1, respectively. The degree distribution with α = 1.0 obeys approximately an exponential form rather than a power-law form.

have demonstrated the extensive existence of scale-free networks. Most of them are growing at all times, while some others are of (almost) fixed size. Most previous models [1–3,12,13] suggested the growth a key ingredients of the emergence of scale-free structure, thus fail to provide an underlying mechanism by which the ungrowing networks exhibiting scale-free property. Here we argue that the rewiring mechanism widely existed in real-life networks like friendship networks [24,25] and brain functional networks [26], maybe the origin of the emergence of scale-free structure in fixed-size networks. Acknowledgement This work is supported by the National Natural Science Foundation of China under Nos. 10635040 and 10472116. B.H.W. acknowledges the support from 973 Project 2006CB705500. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25]

R. Albert, A.-L. Barab´asi, Rev. Modern Phys. 74 (2002) 47. S.N. Dorogovtsev, J.F.F. Mendes, Adv. Phys. 51 (2002) 1079. M.E.J. Newman, SIAM Rev. 45 (2003) 167. M. Faloutsos, P. Faloutsos, C. Faloutsos, Comput. Commun. Rev. 29 (1999) 251. B.A. Huberman, The Laws of the Web, MIT Press, Cambridge, 2001. J. Scott, Social Network Analysis: A Handbook, Sage Publications, London, 2000. T. Zhou, J. Ren, M. Medo, Y.-C. Zhang, Phys. Rev. E 76 (2007) 046115. H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, A.-L. Barab´asi, Nature 407 (2000) 651. S.L. Pimm, Food Webs, University of Chicago, Chicago, 2002. L.da F. Costa, F.A. Rodrigues, G. Travieso, P.R.V. Boas, Adv. Phys. 56 (2007) 167. D.J. Watts, S.H. Strogatz, Nature 393 (1998) 440. A.-L. Barab´asi, R. Albert, Science 286 (1999) 509. A.-L. Barab´asi, R. Albert, H. Jeong, Physica A 272 (1999) 173. Z. Liu, Y.-C. Lai, N. Ye, P. Dasgupta, Phys. Lett. A 303 (2002) 337. P.L. Krapivsky, S. Redner, F. Leyvraz, Phys. Rev. Lett. 85 (2000) 4629. S.N. Dorogovtsev, J.F.F. Mendes, A.N. Samukhim, Phys. Rev. Lett. 85 (2000) 4633. P. Holme, B.J. Kim, Phys. Rev. E 65 (2002) 026107. T. Zhou, G. Yan, B.-H. Wang, Phys. Rev. E 71 (2005) 046141. R.V. Sole, R. Pastor-Satorras, E.D. Smith, T. Kepler, Adv. Complex Syst. 5 (2002) 43. A. Capocci, G. Caldarelli, R. Marchetti, L. Pietronero, Phys. Rev. E. 64 (2001) 035105. J. Berg, M. L¨assig, Phys. Rev. Lett. 89 (2002) 228701. M. Baiesi, S.S. Manna, Phys. Rev. E 68 (2003) 047103. B.J. Kim, A. Trusina, P. Minnhagen, K. Sneppen, Eur. Phys. J. B 43 (2005) 369. A. Rapoport, W.J. Horvath, Behav. Sci. 6 (1961) 279. B. Hu, X.-Y. Jiang, J.-F. Ding, Y.-B. Xie, B.-H. Wang, Physica A 353 (2005) 576.

1688

Y.-B. Xie et al. / Physica A 387 (2008) 1683–1688

[26] V.M. Egu´ıluz, D.R. Chialvo, G.A. Cecchi, M. Baliki, A.V. Apkarian, Phys. Rev. Lett. 94 (2005) 018102. [27] In human brain functional networks, each node represents a voxels of dimension 3 × 3.475 × 3.475 mm3 in the picture taken by functional magnetic resonance imaging (FMRI) in humans. Therefore, the network size is fixed as N = 36 × 64 × 64. [28] K. Park, Y.-C. Lai, N. Ye, Phys. Rev. E 72 (2005) 026131. [29] J. Ohkubo, K. Tanaka, T. Horiguchi, Phys. Rev. E 72 (2005) 036120. [30] T.S. Evans, Eur. Phys. J. B 56 (2007) 65. [31] T.S. Evans, A.D.K. Plato, Phys. Rev. E 75 (2007) 056106. [32] S.N. Dorogovtsev, J.F.F. Mendes, Evolution of Networks - From Biological Nets to the Internet and WWW, Oxford University Press, Oxford, 2003. [33] B. Bollob´as, Random Graphs, Academic Press, New York, 2001.