adjacency_graph(data, directed=False, multigraph=True, attrs={'id': 'id', 'key': 'key'})

Notes

The default value of attrs will be changed in a future release of NetworkX.

Parameters

data : dict

Adjacency list formatted graph data

directed : bool

If True, and direction not specified in data, return a directed graph.

multigraph : bool

If True, and multigraph not specified in data, return a multigraph.

attrs : dict

A dictionary that contains two keys 'id' and 'key'. The corresponding values provide the attribute names for storing NetworkX-internal graph data. The values should be unique. Default value: dict(id='id', key='key') .

Returns

G : NetworkX graph

A NetworkX graph object

Returns graph from adjacency data format.

See Also

adjacency_graph

node_link_data

tree_data

Examples

>>> from networkx.readwrite import json_graph
... G = nx.Graph([(1, 2)])
... data = json_graph.adjacency_data(G)
... H = json_graph.adjacency_graph(data)

See :

Back References

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

networkx.readwrite.json_graph.adjacency.adjacency_data networkx.readwrite.json_graph.adjacency.adjacency_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

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GitHub : /networkx/readwrite/json_graph/adjacency.py#85
type: <class 'function'>
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