johnson

johnson(G, weight='weight')

Uses Johnson’s Algorithm to compute shortest paths.

Johnson’s Algorithm finds a shortest path between each pair of nodes in a weighted graph even if negative weights are present.

Parameters:

GNetworkX graph

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.

Returns:

distancedictionary

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

See also

floyd_warshall_predecessor_and_distance

floyd_warshall_numpy

all_pairs_shortest_path

all_pairs_shortest_path_length

all_pairs_dijkstra_path

bellman_ford_predecessor_and_distance

all_pairs_bellman_ford_path

all_pairs_bellman_ford_path_length

Notes

Johnson’s algorithm is suitable even for graphs with negative weights. It works by using the Bellman–Ford algorithm to compute a transformation of the input graph that removes all negative weights, allowing Dijkstra’s algorithm to be used on the transformed graph.

The time complexity of this algorithm is \(O(n^2 \log n + n m)\), where \(n\) is the number of nodes and \(m\) the number of edges in the graph. For dense graphs, this may be faster than the Floyd–Warshall algorithm.

Examples

>>> graph = nx.DiGraph()
>>> graph.add_weighted_edges_from(
...     [("0", "3", 3), ("0", "1", -5), ("0", "2", 2), ("1", "2", 4), ("2", "3", 1)]
... )
>>> paths = nx.johnson(graph, weight="weight")
>>> paths["0"]["2"]
['0', '1', '2']
----

Additional backends implement this function

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

The parallel computation is implemented by dividing the nodes into chunks and computing the shortest paths using Johnson’s Algorithm for each chunk in parallel.

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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