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Adds a dictionary edge_index which maps edge keys to (u, v, data) tuples.

This is not a complete implementation. For Edmonds algorithm, we only use add_node and add_edge, so that is all that is implemented here. During additions, any specified keys are ignored---this means that you also cannot update edge attributes through add_node and add_edge.

Why do we need this? Edmonds algorithm requires that we track edges, even as we change the head and tail of an edge, and even changing the weight of edges. We must reliably track edges across graph mutations.

MultiDiGraph which assigns unique keys to every edge.

Examples

See :

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


GitHub : /networkx/algorithms/tree/branchings.py#188
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