all_pairs_shortest_path

all_pairs_shortest_path(G, cutoff=None)

Compute shortest paths between all nodes.

Parameters:

GNetworkX graph

cutoffinteger, optional

Depth at which to stop the search. Only paths containing at most cutoff + 1 nodes are returned.

Returns:

pathsiterator

Dictionary, keyed by source and target, of shortest paths.

See also

floyd_warshall

all_pairs_all_shortest_paths

Notes

There may be multiple shortest paths with the same length between two nodes. For each pair, this function returns only one of those paths.

Examples

>>> G = nx.path_graph(5)
>>> path = dict(nx.all_pairs_shortest_path(G))
>>> print(path[0])
{0: [0], 1: [0, 1], 2: [0, 1, 2], 3: [0, 1, 2, 3], 4: [0, 1, 2, 3, 4]}

Only include paths with length less than or equal to the cutoff keyword argument:

>>> path = dict(nx.all_pairs_shortest_path(G, cutoff=2))
>>> print(path[0])
{0: [0], 1: [0, 1], 2: [0, 1, 2]}
----

Additional backends implement this function

cugraph : GPU-accelerated backend.

parallelA networkx backend that uses joblib to run graph algorithms in parallel. Find the nx-parallel’s configuration guide here

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 n_jobs number of 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_jobs number of chunks.

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