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shortest_simple_paths(G, source, target, weight=None)

A simple path is a path with no repeated nodes.

If a weighted shortest path search is to be used, no negative weights are allowed.

Notes

This procedure is based on algorithm by Jin Y. Yen . Finding the first $K$ paths requires $O(KN^3)$ operations.

Parameters

G : NetworkX graph
source : node

Starting node for path

target : node

Ending node for path

weight : string or function

If it is a string, it is the name of the edge attribute to be used as a weight.

If it 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.

If None all edges are considered to have unit weight. Default value None.

Raises

NetworkXNoPath

If no path exists between source and target.

NetworkXError

If source or target nodes are not in the input graph.

NetworkXNotImplemented

If the input graph is a Multi[Di]Graph.

Returns

path_generator: generator

A generator that produces lists of simple paths, in order from shortest to longest.

Generate all simple paths in the graph G from source to target,

starting from shortest ones.

See Also

all_shortest_paths
all_simple_paths
shortest_path

Examples

>>> G = nx.cycle_graph(7)
... paths = list(nx.shortest_simple_paths(G, 0, 3))
... print(paths) [[0, 1, 2, 3], [0, 6, 5, 4, 3]]

You can use this function to efficiently compute the k shortest/best paths between two nodes.

>>> from itertools import islice
... def k_shortest_paths(G, source, target, k, weight=None):
...  return list(
...  islice(nx.shortest_simple_paths(G, source, target, weight=weight), k)
...  )
... for path in k_shortest_paths(G, 0, 3, 2):
...  print(path) [0, 1, 2, 3] [0, 6, 5, 4, 3]
See :

Back References

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

networkx.algorithms.simple_paths.shortest_simple_paths

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/simple_paths.py#424
type: <class 'function'>
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