# Source code for networkx.algorithms.traversal.breadth_first_search

```
"""
====================
Breadth-first search
====================
Basic algorithms for breadth-first searching the nodes of a graph.
"""
import networkx as nx
from collections import defaultdict, deque
__author__ = """\n""".join(['Aric Hagberg <aric.hagberg@gmail.com>'])
__all__ = ['bfs_edges', 'bfs_tree', 'bfs_predecessors', 'bfs_successors']
[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.Graph()
>>> G.add_path([0,1,2])
>>> 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 isinstance(G, nx.DiGraph):
neighbors = G.predecessors_iter
else:
neighbors = G.neighbors_iter
visited = set([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_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.Graph()
>>> G.add_path([0,1,2])
>>> 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.
"""
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):
"""Return dictionary 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: dict
A dictionary with nodes as keys and predecessor nodes as values.
Examples
--------
>>> G = nx.Graph()
>>> G.add_path([0,1,2])
>>> print(nx.bfs_predecessors(G,0))
{1: 0, 2: 1}
Notes
-----
Based on http://www.ics.uci.edu/~eppstein/PADS/BFS.py
by D. Eppstein, July 2004.
"""
return dict((t,s) for s,t in bfs_edges(G,source))
[docs]def bfs_successors(G, source):
"""Return dictionary 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: dict
A dictionary with nodes as keys and list of succssors nodes as values.
Examples
--------
>>> G = nx.Graph()
>>> G.add_path([0,1,2])
>>> print(nx.bfs_successors(G,0))
{0: [1], 1: [2]}
Notes
-----
Based on http://www.ics.uci.edu/~eppstein/PADS/BFS.py
by D. Eppstein, July 2004.
"""
d = defaultdict(list)
for s,t in bfs_edges(G,source):
d[s].append(t)
return dict(d)
```