Research Article | Open Access

# Criterion for Unbounded Synchronous Region in Complex Networks

**Academic Editor:**Jinde Cao

#### Abstract

Synchronization of complex networks has been extensively studied in many fields, where intensive efforts have been devoted to the understanding of its mechanisms. As for discriminating network synchronizability by Master Stability Function method, a dilemma usually encountered is that we have no prior knowledge of the network type that the synchronous region belongs to. In this paper, we investigate a sufficient condition for a general complex dynamical network in the absence of control. A main result is that, when the coupling strength is sufficiently strong, the dynamical network achieves synchronization provided that the symmetric part of the inner-coupling matrix is positive definite. According to our results, synchronous region of the network with positive definite inner-coupling matrix belongs to the unbounded one, and then the eigenvalue of the outer-coupling matrix nearest 0 can be used for judging synchronizability. Even though we cannot gain the necessary and sufficient conditions for synchronizing a network so far, our results constitute a first step toward a better understanding of network synchronization.

#### 1. Introduction

Complex dynamical networks have received increasing attention from different fields in the past two decades. So far, the dynamics of complex networks has been extensively investigated, in which synchronization is a typical topic which has attracted lots of concern [1–17].

As an interesting phenomenon that enables coherent behavior in networks as a result of coupling, synchronization and the discussion upon its sufficient or necessary condition are fundamental and valuable. Pecora and his colleagues used the so-called Master Stability Function (MSF) approach to determine the synchronous region in coupled systems [18, 19], in which the negativeness of Lyapunov Exponent for master stability equation ensures synchronization. Combining MSF approach with Gershörin disk theory, Chen et al. imposed constraints on the coupling strengths to guarantee stability of the synchronous states in coupled dynamical network [20]. These methods, however, obtain just necessary conditions for synchronization due to the fact that Lyapunov Exponent is employed to judge the stability of system.

Zhou et al. and Li and Chen investigated synchronization in general dynamical networks by integrating network models and an adaptive technique and proved that strong enough couplings will synchronize an array of identical cells [11, 12]. To overcome the difficulties caused by too many controllers in large scale complex networks, pinning mechanism is further applied to analyze network synchronization criteria in the works by Zhou et al. and Chen et al. [13, 14]. Research studies on network synchronization mentioned above focused on sufficient conditions, but all of them are gained by introducing controllers.

For general complex dynamical networks in the absence of control, we investigate their sufficient conditions for achieving network synchronization in the current work. Using Lyapunov direct method [21, 22] and matrix theory [23–27], a criterion for synchronization in generally coupled identical systems is proposed. We conclude that network synchronization will be reached when the coupling strength is larger than a threshold, given that the symmetric part of the inner-coupling matrix is positive definite. It is analytically derived in our paper that a network belongs to *Type I* with respect to synchronized region [28], provided with a positive definite inner-coupling matrix.

For discriminating network synchronizability, it is well known that a dilemma is usually encountered in the process of applying MSF method. That is, we have no prior knowledge of the network type that the synchronous region belongs to. Stemmed from our results, the eigenvalue of the outer-coupling matrix nearest can be used for judging synchronizability of a dynamical network with positive definite inner-coupling matrix. Even though we cannot gain the necessary and sufficient conditions for synchronizing a network so far, our results constitute a first step toward a better understanding of network synchronization.

The rest of the paper is organized as follows. In Section 2, a general complex dynamical network model and some mathematical preliminaries are introduced. A sufficient condition for achieving synchronization in the network and detailed discussion are presented in Section 3. Section 4 gives some numerical simulations to show the effectiveness of the proposed synchronization criterion and further illustrates the relationship between synchronous region and our main results. Conclusions are finally drawn in Section 5.

#### 2. Preliminaries

To begin with, we introduce a complex network model describing the dynamical evolution of node states, which is formulated as where , represents the neighborhood of the th node, the state vector of the th node is a continuous function, is a smooth nonlinear vector function, individual node dynamics is , and is the inner-coupling matrix. The outer-coupling weight configuration matrix is symmetric and diffusive satisfying . If there is a link between node and node , then ; otherwise, . In addition, . It is clear that due to the diffusion, with being the eigenvalues of .

*Definition 1. *Let be a solution of the complex dynamical network (1) with initial state . Assume that is continuously differentiable, where . If there is a nonempty subset , with , such that for all , and that
where denotes any norm of a vector or a matrix, then the complex dynamical network (1) is said to achieve *synchronization*.

To develop the main results, a useful hypothesis on the inner-coupling matrix is introduced.

*Assumption 2. *Suppose that , with being eigenvalues of the symmetric part of the inner-coupling matrix .

It suggests that should be a positive definite matrix. This is common for the inner-coupling matrix to satisfy ; for instance, the symmetric part is strictly diagonally dominant.

Since and are symmetric, there exist orthogonal matrices and , such that
where are real numbers and the denotation represents a diagonal matrix whose elements are .

Let be the left eigenvector of the coupling configuration matrix corresponding to the eigenvalue , in which . It is obvious that . Then introducing a weighted mean state of all nodes
one has the following Lemma.

Lemma 3. *For any initial state of model (1), network synchronization is equivalent to .*

*Proof. *On one hand, provided with , one obtains
Thus results in .

On the other hand, if , one has . Owing to the fact that
one gets .

*Remark 4. *Lemma 3 has proved that is a sufficient and necessary condition for network synchronization. In other words, the dynamics of all nodes in the complex network (1) would approach when network synchronization is reached.

Recently, it has been mathematically proved that is a solution of single node dynamical system in the sense of positive limit set [29]
Namely, the synchronous state can be an equilibrium point, a periodic orbit, an aperiodic orbit, or even a chaotic orbit in the phase space.

Define the state error vectors as for all nodes in the network.

Then the error system is given by
according to systems (1) and (4), where .

Denote , , , and . Then one has
where represents the direct product of matrices.

#### 3. Main Results

In this section, a sufficient condition for reaching synchronization in a general complex dynamical network (1) is presented based on Lyapunov direct method and some related matrix theory. Further discussion of the synchronization criterion in detail is also included.

Linearizing the state equation (1) at trajectory , one obtains the variational equation as follows: where is the Jacobian matrix of evaluated at trajectory . Letting , one has Denote as the form , where . Equation (11) can be rewritten as follows: where .

For , one gets the variational equation for the synchronization manifold. Thus one has succeeded in separating from the transverse directions . All the correspond to the transverse eigenvectors. Therefore, the synchronous solution of dynamical network (1) is asymptotically stable if the following system is stable: where .

To deduce the sufficient condition for stability of system (13), the following assumption is one of the basic prerequisites.

*Assumption 5. *Suppose that there exists a positive constant satisfying .

This hypothesis is achievable for a large class of systems depicted by , including linear systems, piecewise linear systems, and numerous chaotic systems (e.g., Chua’s circuit [30], Lorenz family [31–33], etc.).

Theorem 6. *Suppose that Assumptions 2 and 5 hold. The synchronous solution of network (1) is asymptotically stable provided that is larger than , where .*

*Proof. *According to Lemma 3 and the previous discussion, asymptotical stability of synchronous solution of network model (1) can be analyzed by investigating the stability of system (13). Consider a positive semidefinite function as
and regard as a Lyapunov candidate. Then the derivation of along the trajectories of (13) is
Introducing the denotation , one has and ; thus one obtains

In view of and , the derivation of the Lyapunov candidate would be nonpositive given . The largest invariant set of is . According to LaSalle’s invariance principle [21], all the trajectories of system (13) will converge to asymptotically for any initial values. In this set, it is plain to see that for . That means system (13) is stable, and accordingly the synchronous solution of dynamical network (1) is asymptotically stable.

From Theorem 6, we conclude that whether can synchronization of a general complex dynamical network (1) be achieved depends on the relationship between the coupling strength and the constant . The term is associated with individual node dynamics (), inner coupling (), and topology structure () of the whole network.

*Remark 7. *The smaller the is, the larger the coupling strength which leads to network synchronization is. In detail, smaller of single node dynamics or larger of inner-coupling matrix brings about better synchronizability for particular network topology.

*Remark 8. *It is worth noticing that although the previous result assumes that , the threshold of may be much smaller than in reality. In other words, the synchronization criterion for dynamical network (1) is just a sufficient condition.

*Remark 9. *Theorem 6 reveals that for any topology structure, synchronization of network (1) can be achieved when the coupling strength is strong enough, provided that Assumptions 2 and 5 hold. Further, it is seen that the synchronous region of dynamical network (1) belongs to *Type I* from the angle of Master Stability Function method [28] (see Section 4). Accordingly, the eigenvalue of the outer-coupling matrix nearest can be used for judging synchronizability of networks.

*Remark 10. *Although our analysis is founded on a basic hypothesis that the complex network is bidirectionally coupled (the outer-coupling matrix is symmetric), similar conclusions can be drawn for the case in which this hypothesis is relaxed to unidirectional network.

#### 4. Numerical Simulations

To verify the effectiveness of our main results, we choose the node dynamics as Lorenz system and the inner-coupling matrix as in model (1), where represents identity matrix. Topology structures selected in the network are globally coupled network (GCN), star network (SN), and loop network (LN). Then the outer-coupling matrices are , , and , respectively, where

Lorenz system is a typical benchmark chaotic system, which is a simplified mathematical model first developed by Lorenz in 1963 to describe atmospheric convection. The model is a system of three ordinary differential equations now known as the Lorenz equations [31]: which is chaotic when , , and .

It is easy to get that of Lorenz system is and of the inner-coupling matrix is . For dynamical network (1) coupled with nodes, a direct result is Accordingly, because of , one has Then selecting , , and , we have the following error figure to picture the synchronization errors in dynamical network (1), in which topology structures are chosen as GCN, SN, and LN. See Figure 1.

**(a)**

**(b)**

**(c)**

From Figure 1, three networks have all reached synchronization in the condition of , which are consistent with Theorem 6.

According to Remark 8, the condition for network synchronization is just sufficient. To illustrate, let be about of the original coupling strength; say, , , and . It is seen from Figure 2 that synchronization of three networks is achieved as well even if the synchronization criterion is not guaranteed.

**(a)**

**(b)**

**(c)**

Theorem 6 reveals that if Assumptions 2 and 5 hold, synchronization of network (1) can be achieved provided that is sufficiently large. In the case of network synchronization, the real number falls into the synchronous region [28], where is any eigenvalue of the outer-coupling matrix except . Furthermore, in view of Theorem 6, the synchronous region of the network is unbounded, which belongs to *Type I*. If the network belongs to one of the other three types of synchronous region, the coupling strength which leads to synchronization may be upper bounded or even nonexistent. To clarify the unboundedness of synchronous region for the qualified network, three inner-coupling matrices which satisfy Assumption 2 are employed. We choose Lorenz systems as the nodes in dynamical network (1) and coupled them through , , and . Let , , and . It is easy to verify that the symmetric part of the inner-coupling matrices , , and is positive definite. Figure 3 shows the relationship between Lyapunov Exponents (LEs) of system (13) and . During the growth of , LE of system (13) becomes negative when crosses a threshold , and accordingly system (13) is stable. In other words, if synchronization of network (1) is reached, the coupling strength should be larger than a threshold . Predictably, is much weaker than got from Theorem 6. Given coupling configuration structure of a dynamical network, the eigenvalues would be certain, and thus network (1) synchronization would ensure . This is in agreement with Remark 9. On the one hand, the exact threshold of coupling strength for network synchronization is smaller than according to Theorem 6. On the other hand, that is larger than lies in the fact that leads to asynchronization. Although we cannot gain the exact value of so far, our results pave the way for exploring in depth the necessary and sufficient conditions of network synchronization.

**(a)**

**(b)**

**(c)**

#### 5. Conclusions

In conclusion, we have developed a sufficient condition for a general complex dynamical network in the absence of control. We have concluded that if the coupling strength is larger than , synchronization will be reached in the network, where the symmetric part of the inner-coupling matrix is positive definite. In the sense of Master Stability Function method, we have further illustrated that positive eigenvalues of lead to *Type I* network with which synchronous region is unbounded. The findings show that the eigenvalue of the outer-coupling matrix nearest can be used for exploring synchronizability of a dynamical network with positive definite inner-coupling matrix.

#### Conflict of Interests

The author declares that there is no conflict of interests regarding the publication of this paper.

#### Acknowledgment

This work is supported by the National Natural Science Foundation of China under Grants 61004096, 61374173, and 11172215.

#### References

- A. Arenas, A. Díaz-Guilera, and C. J. Pérez-Vicente, “Synchronization reveals topological scales in complex networks,”
*Physical Review Letters*, vol. 96, Article ID 114102, 2006. View at: Publisher Site | Google Scholar - I. Leyva, A. Navas, I. Sendina-Nadal et al., “Explosive transitions to synchronization in networks of phase oscillators,”
*Scientific Reports*, vol. 3, article 1281, 2013. View at: Google Scholar - R. M. Szmoski, R. F. Pereira, and S. E. de Souza Pinto, “Effective dynamics for chaos synchronization in networks with time-varying topology,”
*Communications in Nonlinear Science and Numerical Simulation*, vol. 18, no. 6, pp. 1491–1498, 2013. View at: Publisher Site | Google Scholar | MathSciNet - Y. Wu, C. Li, Y. Wu, and J. Kurths, “Generalized synchronization between two different complex networks,”
*Communications in Nonlinear Science and Numerical Simulation*, vol. 17, no. 1, pp. 349–355, 2012. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - L. M. Pecora and T. L. Carroll, “Synchronization in chaotic systems,”
*Physical Review Letters*, vol. 64, no. 8, pp. 821–824, 1990. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - C. W. Wu and L. O. Chua, “Synchronization in an array of linearly coupled dynamical systems,”
*IEEE Transactions on Circuits and Systems I*, vol. 42, no. 8, pp. 430–447, 1995. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - X. F. Wang and G. Chen, “Synchronization in scale-free dynamical networks: robustness and fragility,”
*IEEE Transactions on Circuits and System I*, vol. 49, no. 1, pp. 54–62, 2002. View at: Publisher Site | Google Scholar | MathSciNet - A. E. Motter, C. Zhou, and J. Kurths, “Network synchronization, diffusion, and the paradox of heterogeneity,”
*Physical Review E*, vol. 71, no. 1, Article ID 016116, 2005. View at: Publisher Site | Google Scholar - J. Cao and F. Ren, “Exponential stability of discrete-time genetic regulatory networks with delays,”
*IEEE Transactions on Neural Networks*, vol. 19, no. 3, pp. 520–523, 2008. View at: Publisher Site | Google Scholar - T. Nishikawa and A. E. Motter, “Maximum performance at minimum cost in network synchronization,”
*Physica D*, vol. 224, no. 1-2, pp. 77–89, 2006. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - J. Zhou, J.-a. Lu, and J. Lü, “Adaptive synchronization of an uncertain complex dynamical network,”
*IEEE Transactions on Automatic Control*, vol. 51, no. 4, pp. 652–656, 2006. View at: Publisher Site | Google Scholar | MathSciNet - Z. Li and G. Chen, “Robust adaptive synchronization of uncertain dynamical networks,”
*Physics Letters A*, vol. 324, no. 2-3, pp. 166–178, 2004. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - J. Zhou, J.-a. Lu, and J. Lü, “Pinning adaptive synchronization of a general complex dynamical network,”
*Automatica*, vol. 44, no. 4, pp. 996–1003, 2008. View at: Publisher Site | Google Scholar | MathSciNet - T. Chen, X. Liu, and W. Lu, “Pinning complex networks by a single controller,”
*IEEE Transactions on Circuits and Systems I*, vol. 54, no. 6, pp. 1317–1326, 2007. View at: Publisher Site | Google Scholar | MathSciNet - W. Yu, J. Cao, and J. Lü, “Global synchronization of linearly hybrid coupled networks with time-varying delay,”
*SIAM Journal on Applied Dynamical Systems*, vol. 7, no. 1, pp. 108–133, 2008. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - W. Yu, G. Chen, and J. Lü, “On pinning synchronization of complex dynamical networks,”
*Automatica*, vol. 45, no. 2, pp. 429–435, 2009. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - W. Yu, G. Chen, J. Lü, and J. Kurths, “Synchronization via pinning control on general complex networks,”
*SIAM Journal on Control and Optimization*, vol. 51, no. 2, pp. 1395–1416, 2013. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - L. M. Pecora and T. L. Carroll, “Master stability functions for synchronized coupled systems,”
*Physical Review Letters*, vol. 80, no. 10, pp. 2109–2112, 1998. View at: Google Scholar - M. Barahona and L. M. Pecora, “Synchronization in small-world systems,”
*Physical Review Letters*, vol. 89, no. 5, Article ID 054101, 4 pages, 2002. View at: Publisher Site | Google Scholar - Y. Chen, G. Rangarajan, and M. Ding, “General stability analysis of synchronized dynamics in coupled systems,”
*Physical Review E*, vol. 67, no. 2, Article ID 026209, 4 pages, 2003. View at: Publisher Site | Google Scholar - K. K. Hassan,
*Nonlinear Systems*, Prentice Hall, 3rd edition, 2002. - A. Isidori,
*Nonlinear Control Systems II*, Electronic Industry Press, 1st edition, 2012 (Chinese). - C. Godsil and G. Royle,
*Algebraic Graph Theory*, vol. 207, Springer, New York, NY, USA, 2001. View at: Publisher Site | MathSciNet - R. A. Horn and C. R. Johnson,
*Matrix Analysis*, Cambridge University Press, New York, NY, USA, 1985. View at: MathSciNet - R. Olfati-Saber and R. M. Murray, “Consensus problems in networks of agents with switching topology and time-delays,”
*IEEE Transactions on Automatic Control*, vol. 49, no. 9, pp. 1520–1533, 2004. View at: Publisher Site | Google Scholar | MathSciNet - C. W. Wu, “Synchronization in networks of nonlinear dynamical systems coupled via a directed graph,”
*Nonlinearity*, vol. 18, no. 3, pp. 1057–1064, 2005. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - W. Lu and T. Chen, “New approach to synchronization analysis of linearly coupled ordinary differential systems,”
*Physica D*, vol. 213, no. 2, pp. 214–230, 2006. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - G. R. Chen, X. F. Wang, and X. Li,
*Introduction to Complex Networks: Models, Structures and Dynamics*, Higher Education Press, 1st edition, 2012. - J. A. Lu, J. Chen, and J. Zhou, “On relations between synchronous state and solution of single node in complex networks,”
*Acta Automatica Sinica*. In press. View at: Google Scholar - T. Matsumoto, L. O. Chua, and M. Komuro, “The double scroll,”
*IEEE Transactions on Circuits and Systems*, vol. 32, no. 8, pp. 797–818, 1985. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - E. N. Lorenz, “Deterministic non-periodic flow,”
*Journal of the Atmospheric Sciences*, vol. 20, pp. 130–141, 1963. View at: Google Scholar - J. Lü and G. Chen, “A new chaotic attractor coined,”
*International Journal of Bifurcation and Chaos in Applied Sciences and Engineering*, vol. 12, no. 3, pp. 659–661, 2002. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - J. Lü, G. Chen, D. Cheng, and S. Celikovsky, “Bridge the gap between the Lorenz system and the Chen system,”
*International Journal of Bifurcation and Chaos in Applied Sciences and Engineering*, vol. 12, no. 12, pp. 2917–2926, 2002. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet

#### Copyright

Copyright © 2013 Jin Zhou. 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.