networkx.algorithms.flow.gomory_hu_tree¶

gomory_hu_tree
(G, capacity='capacity', flow_func=None)[source]¶ Returns the GomoryHu tree of an undirected graph G.
A GomoryHu tree of an undirected graph with capacities is a weighted tree that represents the minimum st cuts for all st pairs in the graph.
It only requires
n1
minimum cut computations instead of the obviousn(n1)/2
. The tree represents all st cuts as the minimum cut value among any pair of nodes is the minimum edge weight in the shortest path between the two nodes in the GomoryHu tree.The GomoryHu tree also has the property that removing the edge with the minimum weight in the shortest path between any two nodes leaves two connected components that form a partition of the nodes in G that defines the minimum st cut.
See Examples section below for details.
 Parameters
G (NetworkX graph) – Undirected graph
capacity (string) – Edges of the graph G are expected to have an attribute capacity that indicates how much flow the edge can support. If this attribute is not present, the edge is considered to have infinite capacity. Default value: ‘capacity’.
flow_func (function) – Function to perform the underlying flow computations. Default value
edmonds_karp()
. This function performs better in sparse graphs with right tailed degree distributions.shortest_augmenting_path()
will perform better in denser graphs.
 Returns
Tree – A NetworkX graph representing the GomoryHu tree of the input graph.
 Return type
NetworkX graph
 Raises
NetworkXNotImplemented – Raised if the input graph is directed.
NetworkXError – Raised if the input graph is an empty Graph.
Examples
>>> G = nx.karate_club_graph() >>> nx.set_edge_attributes(G, 1, "capacity") >>> T = nx.gomory_hu_tree(G) >>> # The value of the minimum cut between any pair ... # of nodes in G is the minimum edge weight in the ... # shortest path between the two nodes in the ... # GomoryHu tree. ... def minimum_edge_weight_in_shortest_path(T, u, v): ... path = nx.shortest_path(T, u, v, weight="weight") ... return min((T[u][v]["weight"], (u, v)) for (u, v) in zip(path, path[1:])) >>> u, v = 0, 33 >>> cut_value, edge = minimum_edge_weight_in_shortest_path(T, u, v) >>> cut_value 10 >>> nx.minimum_cut_value(G, u, v) 10 >>> # The ComoryHu tree also has the property that removing the ... # edge with the minimum weight in the shortest path between ... # any two nodes leaves two connected components that form ... # a partition of the nodes in G that defines the minimum st ... # cut. ... cut_value, edge = minimum_edge_weight_in_shortest_path(T, u, v) >>> T.remove_edge(*edge) >>> U, V = list(nx.connected_components(T)) >>> # Thus U and V form a partition that defines a minimum cut ... # between u and v in G. You can compute the edge cut set, ... # that is, the set of edges that if removed from G will ... # disconnect u from v in G, with this information: ... cutset = set() >>> for x, nbrs in ((n, G[n]) for n in U): ... cutset.update((x, y) for y in nbrs if y in V) >>> # Because we have set the capacities of all edges to 1 ... # the cutset contains ten edges ... len(cutset) 10 >>> # You can use any maximum flow algorithm for the underlying ... # flow computations using the argument flow_func ... from networkx.algorithms import flow >>> T = nx.gomory_hu_tree(G, flow_func=flow.boykov_kolmogorov) >>> cut_value, edge = minimum_edge_weight_in_shortest_path(T, u, v) >>> cut_value 10 >>> nx.minimum_cut_value(G, u, v, flow_func=flow.boykov_kolmogorov) 10
Notes
This implementation is based on Gusfield approach 1 to compute ComoryHu trees, which does not require node contractions and has the same computational complexity than the original method.
See also
References
 1
Gusfield D: Very simple methods for all pairs network flow analysis. SIAM J Comput 19(1):143155, 1990.