Generate a random hierarchic network topologies in respect with the Waxman algorithm.
[g,d,v]=NtgHierWaxmanConnex(a,b,n,l,nl,s,db,dd,cv)
first parameter of the Waxman model.
second parameter of the Waxman model.
network backbone size.
network squared area side.
maximal quantity of nodes per subnetwork.
quantity of network layers.
original diameter of nodes.
diameter difference between successive network layers.
color of each network layer.
network graph.
diameter of each network node.
quantity of nodes per network layer.
NtgHierWaxmanConnex generates a random hierarchic network topology g.
The network backbone of size n 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 to 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 hierarchic structure of the generated network. cv is a s-length vector that contains the colors used to display each 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 node 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.