Document

add_edge(self, u_for_edge, v_for_edge, key=None, **attr)

The nodes u and v will be automatically added if they are not already in the graph.

Edge attributes can be specified with keywords or by directly accessing the edge's attribute dictionary. See examples below.

Notes

To replace/update edge data, use the optional key argument to identify a unique edge. Otherwise a new edge will be created.

NetworkX algorithms designed for weighted graphs cannot use multigraphs directly because it is not clear how to handle multiedge weights. Convert to Graph using edge attribute 'weight' to enable weighted graph algorithms.

Default keys are generated using the method :None:None:`new_edge_key()`. This method can be overridden by subclassing the base class and providing a custom :None:None:`new_edge_key()` method.

Parameters

u_for_edge, v_for_edge : nodes: Nodes can be, for example, strings or numbers. Nodes must be hashable (and not None) Python objects.
key : hashable identifier, optional (default=lowest unused integer): Used to distinguish multiedges between a pair of nodes.
attr : keyword arguments, optional: Edge data (or labels or objects) can be assigned using keyword arguments.

Returns

The edge key assigned to the edge.

Add an edge between u and v.

Examples

The following all add the edge e=(1, 2) to graph G:

>>> G = nx.MultiDiGraph()
... e = (1, 2)
... key = G.add_edge(1, 2)  # explicit two-node form
... G.add_edge(*e)  # single edge as tuple of two nodes
1

>>> G.add_edges_from([(1, 2)])  # add edges from iterable container
[2]

Associate data to edges using keywords:

>>> key = G.add_edge(1, 2, weight=3)
... key = G.add_edge(1, 2, key=0, weight=4)  # update data for key=0
... key = G.add_edge(1, 3, weight=7, capacity=15, length=342.7)

For non-string attribute keys, use subscript notation.

>>> ekey = G.add_edge(1, 2)
... G[1][2][0].update({0: 5})
... G.edges[1, 2, 0].update({0: 5})

See :

Back References

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

networkx.classes.multidigraph.MultiDiGraph.remove_edge networkx.classes.multidigraph.MultiDiGraph networkx.convert_matrix.to_numpy_matrix networkx.classes.multidigraph.MultiDiGraph.to_undirected networkx.classes.multidigraph.MultiDiGraph.add_edge networkx.convert_matrix.to_scipy_sparse_matrix networkx.convert_matrix.to_pandas_adjacency networkx.algorithms.isomorphism.isomorph.is_isomorphic networkx.convert_matrix.to_numpy_array networkx.convert_matrix.to_scipy_sparse_array networkx.classes.multigraph.MultiGraph.to_directed

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