networkx.algorithms.traversal.edgebfs.edge_bfs¶

edge_bfs
(G, source=None, orientation=None)[source]¶ A directed, breadthfirstsearch of edges in
G
, beginning atsource
.Yield the edges of G in a breadthfirstsearch order continuing until all edges are generated.
 Parameters
G (graph) – A directed/undirected graph/multigraph.
source (node, list of nodes) – The node from which the traversal begins. If None, then a source is chosen arbitrarily and repeatedly until all edges from each node in the graph are searched.
orientation (None  ‘original’  ‘reverse’  ‘ignore’ (default: None)) – For directed graphs and directed multigraphs, edge traversals need not respect the original orientation of the edges. When set to ‘reverse’ every edge is traversed in the reverse direction. When set to ‘ignore’, every edge is treated as undirected. When set to ‘original’, every edge is treated as directed. In all three cases, the yielded edge tuples add a last entry to indicate the direction in which that edge was traversed. If orientation is None, the yielded edge has no direction indicated. The direction is respected, but not reported.
 Yields
edge (directed edge) – A directed edge indicating the path taken by the breadthfirstsearch. For graphs,
edge
is of the form(u, v)
whereu
andv
are the tail and head of the edge as determined by the traversal. For multigraphs,edge
is of the form(u, v, key)
, wherekey
is the key of the edge. When the graph is directed, thenu
andv
are always in the order of the actual directed edge. If orientation is not None then the edge tuple is extended to include the direction of traversal (‘forward’ or ‘reverse’) on that edge.
Examples
>>> import networkx as nx >>> nodes = [0, 1, 2, 3] >>> edges = [(0, 1), (1, 0), (1, 0), (2, 0), (2, 1), (3, 1)]
>>> list(nx.edge_bfs(nx.Graph(edges), nodes)) [(0, 1), (0, 2), (1, 2), (1, 3)]
>>> list(nx.edge_bfs(nx.DiGraph(edges), nodes)) [(0, 1), (1, 0), (2, 0), (2, 1), (3, 1)]
>>> list(nx.edge_bfs(nx.MultiGraph(edges), nodes)) [(0, 1, 0), (0, 1, 1), (0, 1, 2), (0, 2, 0), (1, 2, 0), (1, 3, 0)]
>>> list(nx.edge_bfs(nx.MultiDiGraph(edges), nodes)) [(0, 1, 0), (1, 0, 0), (1, 0, 1), (2, 0, 0), (2, 1, 0), (3, 1, 0)]
>>> list(nx.edge_bfs(nx.DiGraph(edges), nodes, orientation='ignore')) [(0, 1, 'forward'), (1, 0, 'reverse'), (2, 0, 'reverse'), (2, 1, 'reverse'), (3, 1, 'reverse')]
>>> list(nx.edge_bfs(nx.MultiDiGraph(edges), nodes, orientation='ignore')) [(0, 1, 0, 'forward'), (1, 0, 0, 'reverse'), (1, 0, 1, 'reverse'), (2, 0, 0, 'reverse'), (2, 1, 0, 'reverse'), (3, 1, 0, 'reverse')]
Notes
The goal of this function is to visit edges. It differs from the more familiar breadthfirstsearch of nodes, as provided by
networkx.algorithms.traversal.breadth_first_search.bfs_edges()
, in that it does not stop once every node has been visited. In a directed graph with edges [(0, 1), (1, 2), (2, 1)], the edge (2, 1) would not be visited if not for the functionality provided by this function.The naming of this function is very similar to bfs_edges. The difference is that ‘edge_bfs’ yields edges even if they extend back to an already explored node while ‘bfs_edges’ yields the edges of the tree that results from a breadthfirstsearch (BFS) so no edges are reported if they extend to already explored nodes. That means ‘edge_bfs’ reports all edges while ‘bfs_edges’ only report those traversed by a nodebased BFS. Yet another description is that ‘bfs_edges’ reports the edges traversed during BFS while ‘edge_bfs’ reports all edges in the order they are explored.
See also
bfs_edges()
,bfs_tree()
,edge_dfs()