scholarly journals Synchronization Problems on Small-world Networks with time delay

Author(s):  
Lei Gu ◽  
Jinjin Shi
2010 ◽  
Vol 51 (8) ◽  
pp. 082701 ◽  
Author(s):  
Lei Gu ◽  
Xiao-Dong Zhang ◽  
Qing Zhou

2007 ◽  
Vol 10 (supp01) ◽  
pp. 85-110 ◽  
Author(s):  
CHRISTIAN DARABOS ◽  
MARIO GIACOBINI ◽  
MARCO TOMASSINI

We investigate the performances of collective task-solving capabilities and the robustness of complex networks of automata using the density and synchronization problems as typical cases. We show by computer simulations that evolved Watts–Strogatz small-world networks have superior performance with respect to several kinds of scale-free graphs. In addition, we show that Watts–Strogatz networks are as robust in the face of random perturbations, both transient and permanent, as configuration scale-free networks, while being widely superior to Barabási–Albert networks. This result differs from information diffusion on scale-free networks, where random faults are highly tolerated by similar topologies.


2009 ◽  
Vol 20 (10) ◽  
pp. 1521-1529 ◽  
Author(s):  
YAN-LONG LI ◽  
JUN MA ◽  
YAN-JUN LIU ◽  
LI-PING ZHANG ◽  
JUN-YAN SU

The order parameters and synchronization are numerically investigated in the time delay small-world connected FitzHugh–Nagumo excitable systems. The simulations show that the order parameter continuously decreases with increasing D, the quality of the synchronization worsens for large noise intensity. As the coupling intensity goes up, the quality of the synchronization worsens also, and find that the larger rewiring probability becomes, the larger-order parameter. At the same time, the order parameter goes up, the time delay declines. We obtained the complete phase diagram for a wide range of values of noise intensity D and control parameter g.


Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.


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