dijkstra_path_length(G, source, target, weight='weight')
Uses Dijkstra's Method to compute the shortest weighted path length between two nodes in a graph.
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.
The function single_source_dijkstra
computes both path and length-of-path if you need both, use that.
starting node for path
ending node for path
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.
If no path exists between source and target.
Shortest path length.
Returns the shortest weighted path length in G from source to target.
>>> G = nx.path_graph(5)See :
... nx.dijkstra_path_length(G, 0, 4) 4
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length
networkx.algorithms.shortest_paths.weighted.dijkstra_path_length
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
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