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positive integer, larger than 1
real number between 0.0 and 1.0
list of vertices
sequence of options (see below)
RandomNetwork(n,p) creates a directed unweighted network on n vertices. The larger p is, the larger the number of levels in the network.
RandomNetwork(V,p) does the same thing except that the vertex labels are chosen from the list V.
If the option acyclic is specified, a random acyclic network is created.
You can optionally specify q which is a real number between 0.0 and 1.0. The result is a random network such that each possible arc is present with probability q. The default value for q is 0.5.
If the option weights=m..n is specified, where m <= n are integers, the network is a weighted graph with edge weights chosen from [m,n] uniformly at random. The weight matrix W in the graph has datatype=integer, and if the edge from vertex i to j is not in the graph then W[i,j] = 0.
If the option weights=x..y where x <= y are decimals is specified, the network is a weighted graph with numerical edge weights chosen from [x,y] uniformly at random. The weight matrix W in the graph has datatype=float, that is, double precision floats (16 decimal digits), and if the edge from vertex i to j is not in the graph then W[i,j] = 0.0.
If the option weights=f where f is a function (a Maple procedure) that returns a number (integer, rational, or decimal number), then f is used to generate the edge weights. The weight matrix W in the network has datatype=anything, and if the edge from vertex i to j is not in the graph then W[i,j] = 0.
The random number generator used can be seeded using the randomize function.
N:=Graph 1: a directed unweighted graph with 10 vertices and 28 arc(s)
N:=Graph 2: a directed unweighted graph with 5 vertices and 6 arc(s)
N:=Graph 3: a directed weighted graph with 10 vertices and 31 arc(s)
AssignEdgeWeights, GraphTheory[DrawGraph], GraphTheory[DrawNetwork], GraphTheory[IsNetwork], GraphTheory[MaxFlow], RandomBipartiteGraph, RandomDigraph, RandomGraph, RandomTournament, RandomTree
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