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Note

This documents the development version of NetworkX. Documentation for the current release can be found here.

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networkx.algorithms.traversal.beamsearch.bfs_beam_edges

bfs_beam_edges(G, source, value, width=None)[source]

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))