scholarly journals Synchronization in a Novel Local-World Dynamical Network Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jianeng Tang ◽  
Peizhong Liu
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Network Model ◽  
Optimal Growth ◽  
Dynamical Networks ◽  
Dynamical Network ◽  
Network Research ◽  
Topology Model ◽  
The Relationship ◽  
Random Failures

Advances in complex network research have recently stimulated increasing interests in understanding the relationship between the topology and dynamics of complex networks. In the paper, we study the synchronizability of a class of local-world dynamical networks. Then, we have proposed a local-world synchronization-optimal growth topology model. Compared with the local-world evolving network model, it exhibits a stronger synchronizability. We also investigate the robustness of the synchronizability with respect to random failures and the fragility of the synchronizability with specific removal of nodes.

COMPLEX NETWORKS: TOPOLOGY, DYNAMICS AND SYNCHRONIZATION

2002 ◽  
Vol 12 (05) ◽  
pp. 885-916 ◽  
Author(s):  
XIAO FAN WANG
Keyword(s):  
Complex Networks ◽  
Network Models ◽  
Small World ◽  
Dynamical Networks ◽  
Scale Free ◽  
Epidemic Dynamics ◽  
The Past ◽  
The Relationship

Dramatic advances in the field of complex networks have been witnessed in the past few years. This paper reviews some important results in this direction of rapidly evolving research, with emphasis on the relationship between the dynamics and the topology of complex networks. Basic quantities and typical examples of various complex networks are described; and main network models are introduced, including regular, random, small-world and scale-free models. The robustness of connectivity and the epidemic dynamics in complex networks are also evaluated. To that end, synchronization in various dynamical networks are discussed according to their regular, small-world and scale-free connections.

Building Complex Network Similar to Facebook

2014 ◽  
Vol 513-517 ◽  
pp. 909-913
Author(s):  
Dong Wei Guo ◽  
Xiang Yan Meng ◽  
Cai Fang Hou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Complex Networks ◽  
Complex Network ◽  
Network Model ◽  
Clustering Coefficient ◽  
Network Characteristics ◽  
Modeling Methods ◽  
Community Strength ◽  
Strength Based

Social networks have been developed rapidly, especially for Facebook which is very popular with 10 billion users. It is a considerable significant job to build complex network similar to Facebook. There are many modeling methods of complex networks but which cant describe characteristics similar to Facebook. This paper provide a building method of complex networks with tunable clustering coefficient and community strength based on BA network model to imitate Facebook. The strategies of edge adding based on link-via-triangular, link-via-BA and link-via-type are used to build a complex network with tunable clustering coefficient and community strength. Under different parameters, statistical properties of the complex network model are analyzed. The differences and similarities are studied among complex network model proposed by this paper and real social network on Facebook. It is found that the network characteristics of the network model and real social network on Facebook are similar under some specific parameters. It is proved that the building method of complex networks is feasible.

scholarly journals A New Model for Complex Dynamical Networks Considering Random Data Loss

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 797
Author(s):  
Xu Wu ◽  
Guo-Ping Jiang ◽  
Xinwei Wang
Keyword(s):  
Network Model ◽  
Network Models ◽  
Lyapunov Stability Theory ◽  
Complex Dynamical Networks ◽  
Output Coupling ◽  
Data Loss ◽  
Complex Dynamical Network ◽  
Dynamical Networks ◽  
Dynamical Network ◽  
Proposed Model

Model construction is a very fundamental and important issue in the field of complex dynamical networks. With the state-coupling complex dynamical network model proposed, many kinds of complex dynamical network models were introduced by considering various practical situations. In this paper, aiming at the data loss which may take place in the communication between any pair of directly connected nodes in a complex dynamical network, we propose a new discrete-time complex dynamical network model by constructing an auxiliary observer and choosing the observer states to compensate for the lost states in the coupling term. By employing Lyapunov stability theory and stochastic analysis, a sufficient condition is derived to guarantee the compensation values finally equal to the lost values, namely, the influence of data loss is finally eliminated in the proposed model. Moreover, we generalize the modeling method to output-coupling complex dynamical networks. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed model.

Cluster Mixed Synchronization of Complex Networks with Disturbance

2015 ◽  
Vol 719-720 ◽  
pp. 448-451
Author(s):  
Li Jie Zeng
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Numerical Simulations ◽  
Network Model ◽  
Complex Dynamical Networks ◽  
Time Varying ◽  
Dynamical Networks ◽  
Bounded Disturbances ◽  
Synchronization Scheme ◽  
Theoretical Results

In this paper, we investigate the cluster mixed synchronization scheme in time-varying delays coupled complex dynamical networks with disturbance. Basing on the community structure of the networks, some sufficient criteria are derived to ensure cluster mixed synchronization of the network model. Particularly, unknown bounded disturbances can be conquered by the proposed control. The numerical simulations are performed to verify the effectiveness of the theoretical results

scholarly journals Computation in Complex Networks

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 192
Author(s):  
Clara Pizzuti ◽  
Annalisa Socievole
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Special Issue ◽  
Network Research

The Special Issue on “Computation in Complex Networks” focused on gathering highly original papers in the field of current complex network research [...]

scholarly journals STRENGTH DISTRIBUTION IN DERIVATIVE NETWORKS

2005 ◽  
Vol 16 (07) ◽  
pp. 1097-1105 ◽  
Author(s):  
LUCIANO DA FONTOURA COSTA ◽  
GONZALO TRAVIESO
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Lower Limit ◽  
Network Model ◽  
Power Law ◽  
Neuronal Networks ◽  
Strength Distribution ◽  
Node Degree ◽  
Upper Limit ◽  
The Difference

This article describes a complex network model whose weights are proportional to the difference between uniformly distributed "fitness" values assigned to the nodes. It is shown both analytically and experimentally that the strength density (i.e., the weighted node degree) for this model, called derivative complex networks, follows a power law with exponent γ<1 if the fitness has an upper limit and γ>1 if the fitness has no upper limit but a positive lower limit. Possible implications for neuronal networks topology and dynamics are also discussed.

scholarly journals Cluster Anti-Synchronization of Complex Networks with Nonidentical Dynamical Nodes

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Shuguo Wang ◽  
Chunyuan He ◽  
Hongxing Yao
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Numerical Simulations ◽  
Network Model ◽  
Complex Dynamical Networks ◽  
Time Varying ◽  
Dynamical Networks ◽  
Nonidentical Nodes ◽  
Theoretical Results

This paper investigates a new cluster antisynchronization scheme in the time-varying delays coupled complex dynamical networks with nonidentical nodes. Based on the community structure of the networks, the controllers are designed differently between the nodes in one community that have direct connections to the nodes in other communities and the nodes without direct connections with the nodes in other communities strategy; some sufficient criteria are derived to ensure cluster anti-synchronization of the network model. Particularly, the weight configuration matrix is not assumed to be irreducible. The numerical simulations are performed to verify the effectiveness of the theoretical results.

scholarly journals Cluster Synchronization of Time-Varying Delays Coupled Complex Networks with Nonidentical Dynamical Nodes

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Shuguo Wang ◽  
Hongxing Yao ◽  
Mingping Sun
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Numerical Simulations ◽  
Network Model ◽  
Complex Dynamical Networks ◽  
Cluster Synchronization ◽  
Time Varying ◽  
Dynamical Networks ◽  
Synchronization Scheme ◽  
Theoretical Results

This paper investigates a new cluster synchronization scheme in the nonlinear coupled complex dynamical networks with nonidentical nodes. The controllers are designed based on the community structure of the networks; some sufficient criteria are derived to ensure cluster synchronization of the network model. Particularly, the weight configuration matrix is not assumed to be symmetric, irreducible. The numerical simulations are performed to verify the effectiveness of the theoretical results.

PINNING IMPULSIVE SYNCHRONIZATION OF COMPLEX DYNAMICAL NETWORKS

2012 ◽  
Vol 22 (10) ◽  
pp. 1250239 ◽  
Author(s):  
NARIMAN MAHDAVI ◽  
MOHAMMAD B. MENHAJ ◽  
JÜRGEN KURTHS ◽  
JIANQUAN LU ◽  
AHMAD AFSHAR
Keyword(s):  
Efficient Algorithm ◽  
Small World ◽  
Complex Dynamical Networks ◽  
Dynamical Networks ◽  
Scale Free ◽  
Dynamical Network ◽  
Impulsive Synchronization ◽  
Strongly Connected ◽  
Successful Control ◽  
The Relationship

In this paper, the problem of pinning impulsive synchronization for complex dynamical networks with directed or undirected but a strongly connected topology is investigated. To remedy this problem, we propose an efficient algorithm to find certain suitable nodes to be controlled via pinning and these selected nodes, in general, could be different at distinct impulsive time instants. The proposed algorithm guarantees the efficiency of the designed pinning impulsive strategy for the global exponential synchronization of state-coupled dynamical networks under an easily-verified condition. In other words, impulsive controllers and an efficient algorithm are designed to control a small fraction of the nodes, which successfully controls the whole dynamical network. Furthermore, we also estimate the upper bound of the number of pinning nodes, which is shown to be closely related to the impulsive intervals. The relationship implies that the required number of pinning nodes, which should be controlled for the successful control of the whole dynamical network, can be greatly reduced by reducing the impulses interval. Finally, simulations of scale-free and small-world networks are given to illustrate the effectiveness of the theoretical results.

scholarly journals Passivity Analysis of Complex Delayed Dynamical Networks with Output Coupling

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Yan-Fang Kang ◽  
Lu-Xian Fang ◽  
Yue-Hui Zhao ◽  
Shun-Yan Ren
Keyword(s):  
Network Model ◽  
Sufficient Conditions ◽  
Output Coupling ◽  
Lyapunov Functionals ◽  
Complex Dynamical Network ◽  
Dynamical Networks ◽  
Numerical Examples ◽  
Dynamical Network ◽  
Passivity Analysis ◽  
Delayed Dynamical Networks

A new complex dynamical network model with output coupling is proposed. This paper is concerned with input passivity and output passivity of the proposed network model. By constructing new Lyapunov functionals, some sufficient conditions ensuring the input passivity and output passivity are obtained. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed results.

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