one_exchange(G, initial_cut=None, seed=None, weight=None)
Use a greedy one exchange strategy to find a locally maximal cut and its value, it works by finding the best node (one that gives the highest gain to the cut value) to add to the current cut and repeats this process until no improvement can be made.
Graph to find a maximum cut for.
Cut to use as a starting point. If not supplied the algorithm starts with an empty cut.
Indicator of random number generation state. See Randomness<randomness>
.
Edge attribute key to use as weight. If not specified, edges have weight one.
Value of the maximum cut.
A partitioning of the nodes that defines a maximum cut.
Compute a partitioning of the graphs nodes and the corresponding cut value.
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them