parse_graphml(graphml_string, node_type=<class 'str'>, edge_key_type=<class 'int'>, force_multigraph=False)
Default node and edge attributes are not propagated to each node and edge. They can be obtained from :None:None:`G.graph` and applied to node and edge attributes if desired using something like this:
>>> default_color = G.graph["node_default"]["color"] # doctest: +SKIP >>> for node, data in G.nodes(data=True): # doctest: +SKIP ... if "color" not in data: ... data["color"] = default_color >>> default_color = G.graph["edge_default"]["color"] # doctest: +SKIP >>> for u, v, data in G.edges(data=True): # doctest: +SKIP ... if "color" not in data: ... data["color"] = default_color
This implementation does not support mixed graphs (directed and unidirected edges together), hypergraphs, nested graphs, or ports.
For multigraphs the GraphML edge "id" will be used as the edge key. If not specified then they "key" attribute will be used. If there is no "key" attribute a default NetworkX multigraph edge key will be provided.
String containing graphml information (e.g., contents of a graphml file).
Convert node ids to this type
Convert graphml edge ids to this type. Multigraphs use id as edge key. Non-multigraphs add to edge attribute dict with name "id".
If True, return a multigraph with edge keys. If False (the default) return a multigraph when multiedges are in the graph.
If no parallel edges are found a Graph or DiGraph is returned. Otherwise a MultiGraph or MultiDiGraph is returned.
Read graph in GraphML format from string.
>>> G = nx.path_graph(4)
... linefeed = chr(10) # linefeed =
>>> s = linefeed.join(nx.generate_graphml(G))See :
... H = nx.parse_graphml(s)
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.readwrite.graphml.parse_graphml
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