networkx.algorithms.connectivity.stoerwagner.stoer_wagner¶
- stoer_wagner(G, weight='weight', heap=<class 'networkx.utils.heaps.BinaryHeap'>)[source]¶
Returns the weighted minimum edge cut using the Stoer-Wagner algorithm.
Determine the minimum edge cut of a connected graph using the Stoer-Wagner algorithm. In weighted cases, all weights must be nonnegative.
The running time of the algorithm depends on the type of heaps used:
Type of heap
Running time
Binary heap
\(O(n (m + n) \log n)\)
Fibonacci heap
\(O(nm + n^2 \log n)\)
Pairing heap
\(O(2^{2 \sqrt{\log \log n}} nm + n^2 \log n)\)
- Parameters
- GNetworkX graph
Edges of the graph are expected to have an attribute named by the weight parameter below. If this attribute is not present, the edge is considered to have unit weight.
- weightstring
Name of the weight attribute of the edges. If the attribute is not present, unit weight is assumed. Default value: ‘weight’.
- heapclass
Type of heap to be used in the algorithm. It should be a subclass of
MinHeap
or implement a compatible interface.If a stock heap implementation is to be used,
BinaryHeap
is recommended overPairingHeap
for Python implementations without optimized attribute accesses (e.g., CPython) despite a slower asymptotic running time. For Python implementations with optimized attribute accesses (e.g., PyPy),PairingHeap
provides better performance. Default value:BinaryHeap
.
- Returns
- cut_valueinteger or float
The sum of weights of edges in a minimum cut.
- partitionpair of node lists
A partitioning of the nodes that defines a minimum cut.
- Raises
- NetworkXNotImplemented
If the graph is directed or a multigraph.
- NetworkXError
If the graph has less than two nodes, is not connected or has a negative-weighted edge.
Examples
>>> G = nx.Graph() >>> G.add_edge("x", "a", weight=3) >>> G.add_edge("x", "b", weight=1) >>> G.add_edge("a", "c", weight=3) >>> G.add_edge("b", "c", weight=5) >>> G.add_edge("b", "d", weight=4) >>> G.add_edge("d", "e", weight=2) >>> G.add_edge("c", "y", weight=2) >>> G.add_edge("e", "y", weight=3) >>> cut_value, partition = nx.stoer_wagner(G) >>> cut_value 4