all_pairs_dijkstra_path

all_pairs_dijkstra_path(G, cutoff=None, weight='weight')

Compute shortest paths between all nodes in a weighted graph.

Parameters:

GNetworkX graph

cutoffinteger or float, optional

Length (sum of edge weights) at which the search is stopped. If cutoff is provided, only return paths with summed weight <= cutoff.

weightstring 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 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 or None to indicate a hidden edge.

Returns:

pathsiterator

(source, dictionary) iterator with dictionary keyed by target and shortest path as the key value.

See also

floyd_warshall, all_pairs_bellman_ford_path

Notes

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

Examples

>>> G = nx.path_graph(5)
>>> path = dict(nx.all_pairs_dijkstra_path(G))
>>> path[0][4]
[0, 1, 2, 3, 4]

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Additional backends implement this function

parallelParallel backend for NetworkX algorithms

The parallel implementation first divides the nodes into chunks and then creates a generator to lazily compute shortest paths for each node_chunk, and then employs joblib’s Parallel function to execute these computations in parallel across all available CPU cores.

Additional parameters:

get_chunksstr, function (default = “chunks”)

A function that takes in an iterable of all the nodes as input and returns an iterable node_chunks. The default chunking is done by slicing the G.nodes into n chunks, where n is the number of CPU cores.

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