GraphTheory[RandomGraphs][RandomNetwork]
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Calling Sequence
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RandomNetwork(n,p,options)
RandomNetwork(n,p,q,options)
RandomNetwork(V,p,options)
RandomNetwork(V,p,q,options)
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Parameters
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n
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-
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positive integer, larger than 1
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p
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-
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real number between 0.0 and 1.0
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V
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list of vertices
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q
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-
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real number between 0.0 and 1.0
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options
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sequence of options (see below)
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Description
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RandomNetwork(n,p) creates a directed unweighted network on n vertices. The larger p is, the larger the number of levels in the network.
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RandomNetwork(V,p) does the same thing except that the vertex labels are chosen from the list V.
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If the option acyclic is specified, a random acyclic network is created.
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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.
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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.
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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[8], 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.
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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.
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The random number generator used can be seeded using the randomize function.
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Examples
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