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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