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.
The generator algorithm is not guaranteed to produce a graph.
Sequence of degrees
Indicator of random number generation state. See Randomness<randomness>
.
Maximum number of tries to create a graph
If the degree sequence is not graphical.
If a graph is not produced in specified number of tries
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.
>>> sequence = [1, 2, 2, 3]See :
... G = nx.random_degree_sequence_graph(sequence, seed=42)
... sorted(d for n, d in G.degree()) [1, 2, 2, 3]
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.generators.degree_seq.random_degree_sequence_graph
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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