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dijkstra_path(G, source, target, weight='weight')

Uses Dijkstra's Method to compute the shortest weighted path between two nodes in a graph.

Notes

Edge weight attributes must be numerical. Distances are calculated as sums of weighted edges traversed.

The weight function can be used to hide edges by returning None. So weight = lambda u, v, d: 1 if d['color']=="red" else None will find the shortest red path.

The weight function can be used to include node weights.

>>> def func(u, v, d):
...     node_u_wt = G.nodes[u].get("node_weight", 1)
...     node_v_wt = G.nodes[v].get("node_weight", 1)
...     edge_wt = d.get("weight", 1)
...     return node_u_wt / 2 + node_v_wt / 2 + edge_wt

In this example we take the average of start and end node weights of an edge and add it to the weight of the edge.

The function single_source_dijkstra computes both path and length-of-path if you need both, use that.

Parameters

G : NetworkX graph
source : node

Starting node

target : node

Ending node

weight : string or function

If this is a string, then edge weights will be accessed via the edge attribute with this key (that is, the weight of the edge joining u to :None:None:`v` will be G.edges[u, v][weight] ). If no such edge attribute exists, the weight of the edge is assumed to be one.

If this is a function, the weight of an edge is the value returned by the function. The function must accept exactly three positional arguments: the two endpoints of an edge and the dictionary of edge attributes for that edge. The function must return a number.

Raises

NodeNotFound

If :None:None:`source` is not in G.

NetworkXNoPath

If no path exists between source and target.

Returns

path : list

List of nodes in a shortest path.

Returns the shortest weighted path from source to target in G.

See Also

bellman_ford_path
bidirectional_dijkstra
single_source_dijkstra

Examples

>>> G = nx.path_graph(5)
... print(nx.dijkstra_path(G, 0, 4)) [0, 1, 2, 3, 4]
See :

Back References

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

networkx.algorithms.shortest_paths.astar.astar_path networkx.algorithms.shortest_paths.weighted.dijkstra_path networkx.algorithms.shortest_paths.weighted.bellman_ford_path

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/shortest_paths/weighted.py#81
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