bfs_predecessors(G, source, depth_limit=None, sort_neighbors=None)
Based on http://www.ics.uci.edu/~eppstein/PADS/BFS.py by D. Eppstein, July 2004. The modifications to allow depth limits based on the Wikipedia article ":None:None:`Depth-limited-search`".
<Unimplemented 'target' '.. _Depth-limited-search: https://en.wikipedia.org/wiki/Depth-limited_search'>
Specify starting node for breadth-first search
Specify the maximum search depth
A function that takes the list of neighbors of given node as input, and returns an iterator over these neighbors but with custom ordering.
(node, predecessor) iterator where predecessor
is the predecessor of :None:None:`node` in a breadth first search starting from :None:None:`source`.
Returns an iterator of predecessors in breadth-first-search from source.
>>> G = nx.path_graph(3)
... print(dict(nx.bfs_predecessors(G, 0))) {1: 0, 2: 1}
>>> H = nx.Graph()
... H.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)])
... print(dict(nx.bfs_predecessors(H, 0))) {1: 0, 2: 0, 3: 1, 4: 1, 5: 2, 6: 2}
>>> M = nx.Graph()
... nx.add_path(M, [0, 1, 2, 3, 4, 5, 6])
... nx.add_path(M, [2, 7, 8, 9, 10])
... print(sorted(nx.bfs_predecessors(M, source=1, depth_limit=3))) [(0, 1), (2, 1), (3, 2), (4, 3), (7, 2), (8, 7)]
>>> N = nx.DiGraph()See :
... nx.add_path(N, [0, 1, 2, 3, 4, 7])
... nx.add_path(N, [3, 5, 6, 7])
... print(sorted(nx.bfs_predecessors(N, source=2))) [(3, 2), (4, 3), (5, 3), (6, 5), (7, 4)]
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.traversal.breadth_first_search.bfs_predecessors
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