max_flow {igraph}

Maximum flow in a graph

Description

In a graph where each edge has a given flow capacity the maximal flow between two vertices is calculated.

Usage

max_flow(graph, source, target, capacity = NULL)

Arguments

graph

The input graph.

source

The id of the source vertex.

target

The id of the target vertex (sometimes also called sink).

capacity

Vector giving the capacity of the edges. If this is NULL (the default) then the capacity edge attribute is used. Note that the weight edge attribute is not used by this function.

Details

max_flow() calculates the maximum flow between two vertices in a weighted (i.e. valued) graph. A flow from source to target is an assignment of non-negative real numbers to the edges of the graph, satisfying two properties: (1) for each edge the flow (i.e. the assigned number) is not more than the capacity of the edge (the capacity parameter or edge attribute), (2) for every vertex, except the source and the target the incoming flow is the same as the outgoing flow. The value of the flow is the incoming flow of the target vertex. The maximum flow is the flow of maximum value.

Value

A named list with components:

value

A numeric scalar, the value of the maximum flow.

flow

A numeric vector, the flow itself, one entry for each edge. For undirected graphs this entry is bit trickier, since for these the flow direction is not predetermined by the edge direction. For these graphs the elements of the this vector can be negative, this means that the flow goes from the bigger vertex id to the smaller one. Positive values mean that the flow goes from the smaller vertex id to the bigger one.

cut

A numeric vector of edge ids, the minimum cut corresponding to the maximum flow.

partition1

A numeric vector of vertex ids, the vertices in the first partition of the minimum cut corresponding to the maximum flow.

partition2

A numeric vector of vertex ids, the vertices in the second partition of the minimum cut corresponding to the maximum flow.

stats

A list with some statistics from the push-relabel algorithm. Five integer values currently: nopush is the number of push operations, norelabel the number of relabelings, nogap is the number of times the gap heuristics was used, nogapnodes is the total number of gap nodes omitted because of the gap heuristics and nobfs is the number of times a global breadth-first-search update was performed to assign better height (=distance) values to the vertices.

References

A. V. Goldberg and R. E. Tarjan: A New Approach to the Maximum Flow Problem Journal of the ACM 35:921-940, 1988.

See Also

Other flow: dominator_tree(), edge_connectivity(), is_min_separator(), is_separator(), min_cut(), min_separators(), min_st_separators(), st_cuts(), st_min_cuts(), vertex_connectivity()

Examples

E <- rbind(c(1, 3, 3), c(3, 4, 1), c(4, 2, 2), c(1, 5, 1), c(5, 6, 2), c(6, 2, 10))
colnames(E) <- c("from", "to", "capacity")
g1 <- graph_from_data_frame(as.data.frame(E))
max_flow(g1, source = V(g1)["1"], target = V(g1)["2"])