to_networkx_graph(data, create_using=None, multigraph_input=False)
The preferred way to call this is automatically from the class constructor
>>> d = {0: {1: {"weight": 1}}} # dict-of-dicts single edge (0,1)
>>> G = nx.Graph(d)
instead of the equivalent
>>> G = nx.from_dict_of_dicts(d)
Current known types are:
any NetworkX graph dict-of-dicts dict-of-lists container (e.g. set, list, tuple) of edges iterator (e.g. itertools.chain) that produces edges generator of edges Pandas DataFrame (row per edge) 2D numpy array scipy sparse matrix pygraphviz agraph
Graph type to create. If graph instance, then cleared before populated.
If True and data is a dict_of_dicts, try to create a multigraph assuming dict_of_dict_of_lists. If data and create_using are both multigraphs then create a multigraph from a multigraph.
Make a NetworkX graph from a known data structure.
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.convert_matrix
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