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random_degree_sequence_graph(sequence, seed=None, tries=10)

If the maximum degree $d_m$ in the sequence is $O(m^{1/4})$ then the algorithm produces almost uniform random graphs in $O(m d_m)$ time where $m$ is the number of edges.

Notes

The generator algorithm is not guaranteed to produce a graph.

Parameters

sequence : list of integers

Sequence of degrees

seed : integer, random_state, or None (default)

Indicator of random number generation state. See Randomness<randomness> .

tries : int, optional

Maximum number of tries to create a graph

Raises

NetworkXUnfeasible

If the degree sequence is not graphical.

NetworkXError

If a graph is not produced in specified number of tries

Returns

G : Graph

A graph with the specified degree sequence. Nodes are labeled starting at 0 with an index corresponding to the position in the sequence.

Returns a simple random graph with the given degree sequence.

See Also

configuration_model
is_graphical

Examples

>>> sequence = [1, 2, 2, 3]
... G = nx.random_degree_sequence_graph(sequence, seed=42)
... sorted(d for n, d in G.degree()) [1, 2, 2, 3]
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

networkx.generators.degree_seq.random_degree_sequence_graph

Local connectivity graph

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


GitHub : /networkx/generators/degree_seq.py#689
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