Generate a random connex network topology in respect with the Waxman model.
[g]=NtgWaxmanConnex(a,b,n,l)
first parameter of the Waxman model.
second parameter of the Waxman model.
network size.
network squared area side.
network graph.
NtgWaxmanConnex 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), and finally 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. The largest connex component of the generated graph is extracted.