networkx.multi_source_dijkstra

networkx.multi_source_dijkstra(G, sources, target=None, cutoff=None, weight='weight')[source]

Find shortest weighted paths and lengths from a given set of source nodes.

Uses Dijkstra’s algorithm to compute the shortest paths and lengths between one of the source nodes and the given target, or all other reachable nodes if not specified, for a weighted graph.

Parameters:

G : NetworkX graph

sources : non-empty set of nodes

Starting nodes for paths. If this is just a set containing a single node, then all paths computed by this function will start from that node. If there are two or more nodes in the set, the computed paths may begin from any one of the start nodes.

target : node label, optional

Ending node for path

cutoff : integer or float, optional

Depth to stop the search. Only return paths with length <= cutoff.

weight : string 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.edge[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:

distance, path : pair of dictionaries

Returns a tuple of two dictionaries keyed by node. The first dictionary stores distance from one of the source nodes. The second stores the path from one of the sources to that node.
Raises:

ValueError

If sources is empty.

See also

multi_source_dijkstra_path, multi_source_dijkstra_path_length

Notes

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

The weight function can be used to hide edges by returning None. So weight = lambda u, v, d: 1 if d['color']=="red" else None will find the shortest red path.

Based on the Python cookbook recipe (119466) at http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/119466

This algorithm is not guaranteed to work if edge weights are negative or are floating point numbers (overflows and roundoff errors can cause problems).

Examples

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