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Network Topology Generator >> Network Topology Generator > NtgHierWaxmanConnexDist

NtgHierWaxmanConnexDist

Generate a random hierarchic network topology of nodes linearly distributed into layers in respect with the Waxman model.

Calling Sequence

[g,d,v]=NtgHierWaxmanConnexDist(a,b,n,l,nl,s,db,dd,cv)

Parameters

a :

first parameter of the Waxman model.

b :

second parameter of the Waxman model.

n :

network size.

l :

network squared area side.

nl :

maximal quantity of nodes per subnetwork.

s :

quantity of network layers.

db :

original diameter of nodes.

dd :

diameter difference between successive network layers.

cv :

color of each network layer.

g :

network graph.

d :

diameter of each network node.

v :

quantity of nodes per network layer.

Description

NtgWaxmanConnexDist generates a random hierarchic network topology g of size n.

The quantity of nodes per layer is determined by a linear distribution. The first layers have fewer nodes than the latest ones. The network backbone is assumed to be created in respect with the Waxman algorithm of parameters a and b. The most important connex subnetwork is extracted. As a matter of course the topology backbone needs to be fully connected. Thereafter s-1 layers are added according the Waxman algorithm (the same parameters a and b are used for each network layer). New nodes are added by small groups of size randomly selected into the range [ 1 2 3 4 ... nl]. Finally basic users (1-degree) are linked to the last network layer. cv, db and dd are only used to emphasize the hierarchical structure of the generated network. cv is a s-length vector that contains the colors used to display each network layer. The nodes of the first layer have a diameter equal to db. The nodes diameter is constant for a layer, but we reduce dd to its current value when we move to the next layer. For instance, if the starting diameter is 20 for the network backbone, nodes of the layer 2 will have a diameter of 15 if dd rates 5. d finally provides the diameter of each network node. v gathers the quantity of nodes per network layer.

Examples

a=0.3;//first parameter of the Waxman model
b=0.4;//second parameter of the Waxman model
n=100;//network size
l=1000;//network squared area side
nl=5;//maximal quantity of nodes per subnetwork
s=2;//quantity of network layers
db=20;//original diameter of nodes
dd=5;//diameter difference between successive network layers
cv=[2 5 6 1];//color of each network layer
[g,d,v]=NtgHierWaxmanConnexDist(a,b,n,l,nl,s,db,dd,cv);//application of NtgHierWaxmanConnexDist
show_graph(g);

Dependency

Author

http://wwwen.uni.lu/interdisciplinary_centre_for_security_reliability_and_trust

Contact


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