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havel_hakimi_graph(deg_sequence, create_using=None)

Notes

The Havel-Hakimi algorithm constructs a simple graph by successively connecting the node of highest degree to other nodes of highest degree, resorting remaining nodes by degree, and repeating the process. The resulting graph has a high degree-associativity. Nodes are labeled 1,.., len(deg_sequence), corresponding to their position in deg_sequence.

The basic algorithm is from Hakimi and was generalized by Kleitman and Wang .

Parameters

deg_sequence: list of integers :

Each integer corresponds to the degree of a node (need not be sorted).

create_using : NetworkX graph constructor, optional (default=nx.Graph)

Graph type to create. If graph instance, then cleared before populated. Directed graphs are not allowed.

Raises

NetworkXException

For a non-graphical degree sequence (i.e. one not realizable by some simple graph).

Returns a simple graph with given degree sequence constructed using the Havel-Hakimi algorithm.

Examples

See :

Back References

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

networkx.linalg.modularitymatrix.modularity_matrix networkx.linalg.bethehessianmatrix.bethe_hessian_matrix

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#441
type: <class 'function'>
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