The chemical distance distribution in percolation clusters

1985 ◽  
Vol 18 (5) ◽  
pp. L247-L249 ◽  
Author(s):  
S Havlin ◽  
B Trus ◽  
G H Weiss ◽  
D Ben-Avraham
Keyword(s):  
Distance Distribution ◽  
Percolation Clusters ◽  
Chemical Distance

A NEW TYPE OF DYNAMICAL EXCITATIONS FOR PERCOLATION CLUSTERS

Fractals ◽  
1993 ◽  
Vol 01 (04) ◽  
pp. 959-962 ◽  
Author(s):  
M. KOLB
Keyword(s):  
Numerical Simulations ◽  
Three Dimensions ◽  
Bond Percolation ◽  
Scalar Model ◽  
Local Connectivity ◽  
Scaling Properties ◽  
Percolation Clusters ◽  
Chemical Distance ◽  
Dimension Change ◽  
New Type

By increasing the local connectivity of percolation clusters, structures with different scaling properties are generated. Both the topology, characterized by the chemical distance, and the dynamics, measured by the spectral dimension, change. For bond percolation in two (three) dimensions, new exponents have been determined by means of numerical simulations: dmin=1.02(4) (1.09(6)) and, for the scalar model, ds=1.56(7) (1–76(8)).

Transport through bootstrap percolation clusters

1991 ◽  
Vol 1 (5) ◽  
pp. 685-692 ◽  
Author(s):  
Muhammad Sahimi ◽  
Tane S. Ray
Keyword(s):  
Bootstrap Percolation ◽  
Percolation Clusters

Distance Distribution between Complex Network Nodes in Hyperbolic Space

2016 ◽  
Vol 25 (3) ◽  
pp. 223-236 ◽  
Author(s):  
Gregorio Alanis-Lobato ◽  
Miguel A. Andrade-Navarro ◽  
Keyword(s):  
Complex Network ◽  
Hyperbolic Space ◽  
Distance Distribution ◽  
Network Nodes

Analytical solution of the PELDOR inverse problem using the integral Mellin transform

2017 ◽  
Vol 19 (48) ◽  
pp. 32381-32388 ◽  
Author(s):  
Anna G. Matveeva ◽  
Vyacheslav M. Nekrasov ◽  
Alexander G. Maryasov
Keyword(s):  
Inverse Problem ◽  
Distribution Function ◽  
Analytical Solution ◽  
Short Distance ◽  
Mellin Transform ◽  
Distance Distribution ◽  
Distance Distribution Function ◽  
Distance Range ◽  
Model Free ◽  
Model Free Approach

The model-free approach used does not introduce systematic distortions in the computed distance distribution function between two spins and appears to result in noise grouping in the short distance range.

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