GraphTheory[RandomGraphs]
RandomDigraph
generate random digraph
Calling Sequence
Parameters
Options
Description
Examples
RandomDigraph(V, p, options)
RandomDigraph(V, m, options)
RandomDigraph(n, p, options)
RandomDigraph(n, m, options)
V
-
list of vertices
n
positive integer
p
numerical value in the closed range [0.0,1.0]
m
non-negative integer
options
(optional) equation(s) of the form option=value where option is one of seed or weights
seed = integer or none
Seed for the random number generator. When an integer is specified, this is equivalent to calling randomize(seed).
weights = range or function
If the option weights=m..n is specified, where m≤n are integers, the graph returned 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 floating-point numbers is specified, the graph returned 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.
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 graph has datatype=anything, and if the edge from vertex i to j is not in the graph then W[i,j] = 0.
RandomDigraph(n,m) creates a directed unweighted graph on n vertices and m edges, where the m edges are chosen uniformly at random.
RandomDigraph(n,p) creates a directed unweighted graph on n vertices where each possible edge is present with probability p.
If the first input is a positive integer n, then the vertices are labeled 1,2,...,n. Alternatively you may specify the vertex labels in a list.
The random number generator used can be seeded with the seed option or by using the randomize function.
with⁡GraphTheory:
with⁡RandomGraphs:
G≔RandomDigraph⁡10,0.5
G≔Graph 1: a directed graph with 10 vertices and 39 arc(s)
IsDirected⁡G
true
H≔RandomDigraph⁡10,20
H≔Graph 2: a directed graph with 10 vertices, 19 arc(s), and 1 self-loop(s)
J≔RandomDigraph⁡4,6,weights=1..4
J≔Graph 3: a directed weighted graph with 4 vertices, 3 arc(s), and 3 self-loop(s)
WeightMatrix⁡J
0400022000330002
See Also
AssignEdgeWeights
GraphTheory:-IsDirected
GraphTheory:-WeightMatrix
RandomBipartiteGraph
RandomGraph
RandomNetwork
RandomTournament
RandomTree
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