_configuration_model(deg_sequence, create_using, directed=False, in_deg_sequence=None, seed=None)
deg_sequence
is a list of nonnegative integers representing the degree of the node whose label is the index of the list element.
create_using
see ~networkx.empty_graph
.
directed
and in_deg_sequence
are required if you want the returned graph to be generated using the directed configuration model algorithm. If directed
is False
, then deg_sequence
is interpreted as the degree sequence of an undirected graph and in_deg_sequence
is ignored. Otherwise, if directed
is True
, then deg_sequence
is interpreted as the out-degree sequence and in_deg_sequence
as the in-degree sequence of a directed graph.
deg_sequence
and in_deg_sequence
need not be the same length.
seed
is a random.Random or numpy.random.RandomState instance
This function returns a graph, directed if and only if directed
is True
, generated according to the configuration model algorithm. For more information on the algorithm, see the configuration_model
or directed_configuration_model
functions.
Helper function for generating either undirected or directed configuration model graphs.
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them