networkx.generators.geometric.random_geometric_graph¶

random_geometric_graph
(n, radius, dim=2, pos=None, p=2, seed=None)[source]¶ Returns a random geometric graph in the unit cube of dimensions
dim
.The random geometric graph model places
n
nodes uniformly at random in the unit cube. Two nodes are joined by an edge if the distance between the nodes is at mostradius
.Edges are determined using a KDTree when SciPy is available. This reduces the time complexity from \(O(n^2)\) to \(O(n)\).
 Parameters
 nint or iterable
Number of nodes or iterable of nodes
 radius: float
Distance threshold value
 dimint, optional
Dimension of graph
 posdict, optional
A dictionary keyed by node with node positions as values.
 pfloat, optional
Which Minkowski distance metric to use.
p
has to meet the condition1 <= p <= infinity
.If this argument is not specified, the \(L^2\) metric (the Euclidean distance metric), p = 2 is used. This should not be confused with the
p
of an ErdősRényi random graph, which represents probability. seedinteger, random_state, or None (default)
Indicator of random number generation state. See Randomness.
 Returns
 Graph
A random geometric graph, undirected and without selfloops. Each node has a node attribute
'pos'
that stores the position of that node in Euclidean space as provided by thepos
keyword argument or, ifpos
was not provided, as generated by this function.
Notes
This uses a kd tree to build the graph.
The
pos
keyword argument can be used to specify node positions so you can create an arbitrary distribution and domain for positions.For example, to use a 2D Gaussian distribution of node positions with mean (0, 0) and standard deviation 2:
>>> import random >>> n = 20 >>> pos = {i: (random.gauss(0, 2), random.gauss(0, 2)) for i in range(n)} >>> G = nx.random_geometric_graph(n, 0.2, pos=pos)
References
 1
Penrose, Mathew, Random Geometric Graphs, Oxford Studies in Probability, 5, 2003.
Examples
Create a random geometric graph on twenty nodes where nodes are joined by an edge if their distance is at most 0.1:
>>> G = nx.random_geometric_graph(20, 0.1)