- shortest_simple_paths(G, source, target, weight=None)[source]#
- Generate all simple paths in the graph G from source to target,
starting from shortest ones.
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.
- GNetworkX graph
Starting node for path
Ending node for path
- weightstring 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.
- path_generator: generator
A generator that produces lists of simple paths, in order from shortest to longest.
If no path exists between source and target.
If source or target nodes are not in the input graph.
If the input graph is a Multi[Di]Graph.
This procedure is based on algorithm by Jin Y. Yen . Finding the first \(K\) paths requires \(O(KN^3)\) operations.
Jin Y. Yen, “Finding the K Shortest Loopless Paths in a Network”, Management Science, Vol. 17, No. 11, Theory Series (Jul., 1971), pp. 712-716.
>>> 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]