parse_graphml¶

parse_graphml(graphml_string, node_type=<class 'str'>, edge_key_type=<class 'int'>, force_multigraph=False)[source]¶

Read graph in GraphML format from string.

Parameters

graphml_stringstring: String containing graphml information (e.g., contents of a graphml file).
node_type: Python type (default: str): Convert node ids to this type
edge_key_type: Python type (default: int): Convert graphml edge ids to this type. Multigraphs use id as edge key. Non-multigraphs add to edge attribute dict with name “id”.
force_multigraphbool (default: False): If True, return a multigraph with edge keys. If False (the default) return a multigraph when multiedges are in the graph.

Returns

graph: NetworkX graph: If no parallel edges are found a Graph or DiGraph is returned. Otherwise a MultiGraph or MultiDiGraph is returned.

Notes

Default node and edge attributes are not propagated to each node and edge. They can be obtained from G.graph and applied to node and edge attributes if desired using something like this:

>>> default_color = G.graph["node_default"]["color"]  
>>> for node, data in G.nodes(data=True):  
...     if "color" not in data:
...         data["color"] = default_color
>>> default_color = G.graph["edge_default"]["color"]  
>>> for u, v, data in G.edges(data=True):  
...     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.

Examples

>>> G = nx.path_graph(4)
>>> linefeed = chr(10)  # linefeed = 

>>> s = linefeed.join(nx.generate_graphml(G))
>>> H = nx.parse_graphml(s)