A fast non-volatile memory aware algorithm for generating random scale-free networks

2017 ◽  
Author(s):  
Cheng-Chin Tu ◽  
Mi-Yen Yeh ◽  
Tei-Wei Kuo
Keyword(s):  
Scale Free ◽  
Scale Free Networks ◽  
Non Volatile Memory ◽  
Volatile Memory ◽  
Random Scale

Asymptotic Behavior and Synchronizability Characteristics of a Class of Recurrent Neural Networks

2007 ◽  
Vol 19 (9) ◽  
pp. 2492-2514 ◽  
Author(s):  
Christof Cebulla
Keyword(s):  
Neural Network ◽  
Asymptotic Behavior ◽  
Recurrent Neural Networks ◽  
Random Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
Zhang Model ◽  
Random Scale ◽  
Connectivity Distribution ◽  
Time Asymptotic Behavior

We propose an approach to the analysis of the influence of the topology of a neural network on its synchronizability in the sense of equal output activity rates given by a particular neural network model. The model we introduce is a variation of the Zhang model. We investigate the time-asymptotic behavior of the corresponding dynamical system (in particular, the conditions for the existence of an invariant compact asymptotic set) and apply the results of the synchronizability analysis on a class of random scale free networks and to the classical random networks with Poisson connectivity distribution.

scholarly journals Fundamental Structural Constraint of Random Scale-Free Networks

2012 ◽  
Vol 109 (11) ◽  
Author(s):  
Yongjoo Baek ◽  
Daniel Kim ◽  
Meesoon Ha ◽  
Hawoong Jeong
Keyword(s):  
Structural Constraint ◽  
Scale Free ◽  
Scale Free Networks ◽  
Random Scale

scholarly journals Clustering of random scale-free networks

2012 ◽  
Vol 86 (2) ◽  
Author(s):  
Pol Colomer-de-Simon ◽  
Marián Boguñá
Keyword(s):  
Scale Free ◽  
Scale Free Networks ◽  
Random Scale

scholarly journals Range-based attacks on links in random scale-free networks

2008 ◽  
Vol 2008 (02) ◽  
pp. P02008 ◽  
Author(s):  
Baihua Gong ◽  
Jun Liu ◽  
Liang Huang ◽  
Kongqing Yang ◽  
Lei Yang
Keyword(s):  
Scale Free ◽  
Scale Free Networks ◽  
Random Scale

scholarly journals Characterizing the intrinsic correlations of scale-free networks

2016 ◽  
Vol 27 (03) ◽  
pp. 1650024 ◽  
Author(s):  
J. B. de Brito ◽  
C. I. N. Sampaio Filho ◽  
A. A. Moreira ◽  
J. S. Andrade
Keyword(s):  
Extreme Values ◽  
Network Models ◽  
Network Size ◽  
Scale Free Network ◽  
Scale Free ◽  
Scale Free Networks ◽  
Free Network ◽  
Degree Correlations ◽  
Multiple Edges ◽  
Random Scale

When studying topological or dynamical properties of random scale-free networks, it is tacitly assumed that degree–degree correlations are not present. However, simple constraints, such as the absence of multiple edges and self-loops, can give rise to intrinsic correlations in these structures. In the same way that Fermionic correlations in thermodynamic systems are relevant only in the limit of low temperature, the intrinsic correlations in scale-free networks are relevant only when the extreme values for the degrees grow faster than the square root of the network size. In this situation, these correlations can significantly affect the dependence of the average degree of the nearest neighbors of a given vertex on this vertices degree. Here, we introduce an analytical approach that is capable to predict the functional form of this property. Moreover, our results indicate that random scale-free network models are not self-averaging, that is, the second moment of their degree distribution may vary orders of magnitude among different realizations. Finally, we argue that the intrinsic correlations investigated here may have profound impact on the critical properties of random scale-free networks.

scholarly journals Generation of uncorrelated random scale-free networks

2005 ◽  
Vol 71 (2) ◽  
Author(s):  
Michele Catanzaro ◽  
Marián Boguñá ◽  
Romualdo Pastor-Satorras
Keyword(s):  
Scale Free ◽  
Scale Free Networks ◽  
Random Scale
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