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NtgWaxman

Generate a random network topology in respect with the Waxman algorithm.

Calling Sequence

[g,d]=NtgWaxman(a,b,n,l)

Parameters

a :

first parameter of the Waxman model.

b :

second parameter of the Waxman model.

n :

network size.

l :

network squared area side.

g :

network graph.

d :

node degree distribution.

Description

NtgWaxman generates a random network topology g in respect with the Waxman algorithm. The Waxman Method (WM) stays the most used model as it provides an accurate representation for real networks at least at the geographic level. A random graph of n nodes uniformly distributed inside a square is generated. The probability p to connect two nodes u and v is given by p(d)=alpha*e^(-d/(b*l). d corresponds to the Euclidean distance between u and v. l is the maximal distance between two random nodes. Generally it rates the network square side where nodes are placed. Two parameters a and b are needed in order to entirely define WM. If a and b belong to [0,1], then p(d) is also included into the range [0,1]. When a increases, the amount of links grows too. The ratio between the quantity of long and short links changes in the same manner than b does. Network edges are attributed as what follows. For each set of two distinct network nodes u and v, the distance d between u and v is performed, then p(d) is calculated for the set (u,v). A random value t is generated according to a uniform distribution in [0,1]. If t is inferior to p(d), a link is created between u and v.

Examples

a=0.1;//first parameter of the Waxman model
b=0.8;//second parameter of the Waxman model
n=100;//network size
l=1000;//network squared area side
[g,d]=NtgWaxman(a,b,n,l);//application of NtgWaxman
show_graph(g);
scf(1);clf(1);
plot(d);
plot2d3(d);
xtitle('','node index','degree');

Dependency

Author

http://wwwen.uni.lu/interdisciplinary_centre_for_security_reliability_and_trust

Contact


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