dijkstra_predecessor_and_distance(G, source, cutoff=None, weight='weight')
Uses Dijkstra's Method to obtain the shortest weighted paths and return dictionaries of predecessors for each node and distance for each node from the :None:None:`source`.
Edge weight attributes must be numerical. Distances are calculated as sums of weighted edges traversed.
The list of predecessors contains more than one element only when there are more than one shortest paths to the key node.
Starting node for path
Length (sum of edge weights) at which the search is stopped. If cutoff is provided, only return paths with summed weight <= cutoff.
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 :None:None:`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.
If :None:None:`source` is not in G.
Returns two dictionaries representing a list of predecessors of a node and the distance to each node.
Compute weighted shortest path length and predecessors.
>>> G = nx.path_graph(5, create_using=nx.DiGraph())
... pred, dist = nx.dijkstra_predecessor_and_distance(G, 0)
... sorted(pred.items()) [(0, []), (1, [0]), (2, [1]), (3, [2]), (4, [3])]
>>> sorted(dist.items()) [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]
>>> pred, dist = nx.dijkstra_predecessor_and_distance(G, 0, 1)
... sorted(pred.items()) [(0, []), (1, [0])]
>>> sorted(dist.items()) [(0, 0), (1, 1)]See :
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
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SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them