# breadth_first_search.py - breadth-first traversal of a graph
#
# Copyright (C) 2004-2016 NetworkX Developers
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
#
# This file is part of NetworkX.
#
# NetworkX is distributed under a BSD license; see LICENSE.txt for more
# information.
#
# Authors:
# Aric Hagberg <aric.hagberg@gmail.com>
#
"""Basic algorithms for breadth-first searching the nodes of a graph."""
import networkx as nx
from collections import deque
__all__ = ['bfs_edges', 'bfs_tree', 'bfs_predecessors', 'bfs_successors']
def generic_bfs_edges(G, source, neighbors=None):
"""Iterates over edges in a breadth-first search.
The breadth-first search begins at `source` and enqueues the
neighbors of newly visited nodes specified by the `neighbors`
function.
Parameters
----------
G : NetworkX graph
source : node
Starting node for the breadth-first search; this function
iterates over only those edges in the component reachable from
this node.
neighbors : function
A function that takes a newly visited node of the graph as input
and returns an *iterator* (not just a list) of nodes that are
neighbors of that node. If not specified, this is just the
``G.neighbors`` method, but in general it can be any function
that returns an iterator over some or all of the neighbors of a
given node, in any order.
Yields
------
edge
Edges in the breadth-first search starting from `source`.
Examples
--------
>>> G = nx.path_graph(3)
>>> print(list(nx.bfs_edges(G,0)))
[(0, 1), (1, 2)]
Notes
-----
This implementation is from `PADS`_, which was in the public domain
when it was first accessed in July, 2004.
.. _PADS: http://www.ics.uci.edu/~eppstein/PADS/BFS.py
"""
visited = {source}
queue = deque([(source, neighbors(source))])
while queue:
parent, children = queue[0]
try:
child = next(children)
if child not in visited:
yield parent, child
visited.add(child)
queue.append((child, neighbors(child)))
except StopIteration:
queue.popleft()
[docs]def bfs_edges(G, source, reverse=False):
"""Produce edges in a breadth-first-search starting at source.
Parameters
----------
G : NetworkX graph
source : node
Specify starting node for breadth-first search and return edges in
the component reachable from source.
reverse : bool, optional
If True traverse a directed graph in the reverse direction
Returns
-------
edges: generator
A generator of edges in the breadth-first-search.
Examples
--------
>>> G = nx.path_graph(3)
>>> print(list(nx.bfs_edges(G,0)))
[(0, 1), (1, 2)]
Notes
-----
Based on http://www.ics.uci.edu/~eppstein/PADS/BFS.py
by D. Eppstein, July 2004.
"""
if reverse and G.is_directed():
successors = G.predecessors
else:
successors = G.neighbors
# TODO In Python 3.3+, this should be `yield from ...`
for e in generic_bfs_edges(G, source, successors):
yield e
[docs]def bfs_tree(G, source, reverse=False):
"""Return an oriented tree constructed from of a breadth-first-search
starting at source.
Parameters
----------
G : NetworkX graph
source : node
Specify starting node for breadth-first search and return edges in
the component reachable from source.
reverse : bool, optional
If True traverse a directed graph in the reverse direction
Returns
-------
T: NetworkX DiGraph
An oriented tree
Examples
--------
>>> G = nx.path_graph(3)
>>> print(list(nx.bfs_tree(G,1).edges()))
[(1, 0), (1, 2)]
Notes
-----
Based on http://www.ics.uci.edu/~eppstein/PADS/BFS.py
by D. Eppstein, July 2004.
"""
T = nx.DiGraph()
T.add_node(source)
T.add_edges_from(bfs_edges(G, source, reverse=reverse))
return T
[docs]def bfs_predecessors(G, source):
"""Returns an iterator of predecessors in breadth-first-search from source.
Parameters
----------
G : NetworkX graph
source : node
Specify starting node for breadth-first search and return edges in
the component reachable from source.
Returns
-------
pred: iterator
(node, predecessors) iterator where predecessors is the list of
predecessors of the node.
Examples
--------
>>> 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)])
>>> dict(nx.bfs_predecessors(H, 0))
{1: 0, 2: 0, 3: 1, 4: 1, 5: 2, 6: 2}
Notes
-----
Based on http://www.ics.uci.edu/~eppstein/PADS/BFS.py
by D. Eppstein, July 2004.
"""
for s, t in bfs_edges(G, source):
yield (t, s)
[docs]def bfs_successors(G, source):
"""Returns an iterator of successors in breadth-first-search from source.
Parameters
----------
G : NetworkX graph
source : node
Specify starting node for breadth-first search and return edges in
the component reachable from source.
Returns
-------
succ: iterator
(node, successors) iterator where successors is the list of
successors of the node.
Examples
--------
>>> G = nx.path_graph(3)
>>> print(dict(nx.bfs_successors(G,0)))
{0: [1], 1: [2]}
>>> H = nx.Graph()
>>> H.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)])
>>> dict(nx.bfs_successors(H, 0))
{0: [1, 2], 1: [3, 4], 2: [5, 6]}
Notes
-----
Based on http://www.ics.uci.edu/~eppstein/PADS/BFS.py
by D. Eppstein, July 2004.
"""
parent = source
children = []
for p, c in bfs_edges(G, source):
if p == parent:
children.append(c)
continue
yield (parent, children)
children = [c]
parent = p
yield (parent, children)