# Source code for networkx.algorithms.traversal.beamsearch

```
# beamsearch.py - breadth-first search with limited queueing
#
# Copyright 2016-2019 NetworkX developers.
#
# This file is part of NetworkX.
#
# NetworkX is distributed under a BSD license; see LICENSE.txt for more
# information.
"""Basic algorithms for breadth-first searching the nodes of a graph."""
import networkx as nx
from .breadth_first_search import generic_bfs_edges
__all__ = ['bfs_beam_edges']
[docs]def bfs_beam_edges(G, source, value, width=None):
"""Iterates over edges in a beam search.
The beam search is a generalized breadth-first search in which only
the "best" *w* neighbors of the current node are enqueued, where *w*
is the beam width and "best" is an application-specific
heuristic. In general, a beam search with a small beam width might
not visit each node in the graph.
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.
value : function
A function that takes a node of the graph as input and returns a
real number indicating how "good" it is. A higher value means it
is more likely to be visited sooner during the search. When
visiting a new node, only the `width` neighbors with the highest
`value` are enqueued (in decreasing order of `value`).
width : int (default = None)
The beam width for the search. This is the number of neighbors
(ordered by `value`) to enqueue when visiting each new node.
Yields
------
edge
Edges in the beam search starting from `source`, given as a pair
of nodes.
Examples
--------
To give nodes with, for example, a higher centrality precedence
during the search, set the `value` function to return the centrality
value of the node::
>>> G = nx.karate_club_graph()
>>> centrality = nx.eigenvector_centrality(G)
>>> source = 0
>>> width = 5
>>> for u, v in nx.bfs_beam_edges(G, source, centrality.get, width):
... print((u, v)) # doctest: +SKIP
"""
if width is None:
width = len(G)
def successors(v):
"""Returns a list of the best neighbors of a node.
`v` is a node in the graph `G`.
The "best" neighbors are chosen according to the `value`
function (higher is better). Only the `width` best neighbors of
`v` are returned.
The list returned by this function is in decreasing value as
measured by the `value` function.
"""
# TODO The Python documentation states that for small values, it
# is better to use `heapq.nlargest`. We should determine the
# threshold at which its better to use `heapq.nlargest()`
# instead of `sorted()[:]` and apply that optimization here.
#
# If `width` is greater than the number of neighbors of `v`, all
# neighbors are returned by the semantics of slicing in
# Python. This occurs in the special case that the user did not
# specify a `width`: in this case all neighbors are always
# returned, so this is just a (slower) implementation of
# `bfs_edges(G, source)` but with a sorted enqueue step.
return iter(sorted(G.neighbors(v), key=value, reverse=True)[:width])
# TODO In Python 3.3+, this should be `yield from ...`
for e in generic_bfs_edges(G, source, successors):
yield e
```