# graph_tool.topology.max_cardinality_matching#

graph_tool.topology.max_cardinality_matching(g, weight=None, bipartite=False, init_match='extra_greedy', heuristic=False, minimize=False, edges=False, brute_force=False)[source]#

Find a maximum cardinality matching in the graph.

Parameters:
gGraph

Graph to be used.

weightEdgePropertyMap (optional, default: None)

If provided, the matching will maximize the sum of edge weights.

bipartitebool or VertexPropertyMap (optional, default: False)

If True, the graph will be assumed to be bipartite. If a VertexPropertyMap is passed, it should correspond to an existing bi-partition.

init_matchstring (optional, default: "extra_greedy")

Initial matching strategy. Can be one of: “empty”, “greedy”, “extra_greedy”. Ignored if weight is given, or heuristic == True.

minimizebool (optional, default: True)

If True, the matching will minimize the weights, otherwise they will be maximized. This option has no effect if heuristic == False.

heuristicbool (optional, default: False)

If True, a random heuristic will be used, which runs in linear time.

edgesbool (optional, default: False)

If True, an edge property map will be returned, instead of a vertex property map.

brute_forcebool (optional, default: False)

If True, and weight is not None and heuristic is False, a slower, brute-force algorithm is used.

Returns:
matchVertexPropertyMap

Vertex property map where the matching is specified. If edges == True a boolean-valued edge property map is returned instead.

Notes

A matching is a subset of the edges of a graph such that no two edges share a common vertex. A maximum cardinality matching has maximum size over all matchings in the graph.

If the parameter weight is provided, a matching with maximum cardinality and maximum weight is returned.

If heuristic == True the algorithm does not necessarily return the maximum matching, instead the focus is to run on linear time [matching-heuristic].

This algorithm runs in time $$O(EV\times\alpha(E,V))$$, where $$\alpha(m,n)$$ is a slow growing function that is at most 4 for any feasible input.

If weights are given, the algorithm runs in time $$O(V^3)$$.

If heuristic == True, the algorithm runs in time $$O(V + E)$$.

If brute_force == True, the algorithm runs in time $$O(exp(E))$$.

For a more detailed description, see [boost-max-matching] and [boost-max-weighted-matching].

References

B. Hendrickson and R. Leland. “A Multilevel Algorithm for Partitioning Graphs.” In S. Karin, editor, Proc. Supercomputing ’95, San Diego. ACM Press, New York, 1995, DOI: 10.1145/224170.224228 [sci-hub, @tor]

Examples

>>> g = gt.GraphView(gt.price_network(300), directed=False)
>>> w = gt.max_cardinality_matching(g, edges=True)
>>> w = w.copy("double")
>>> w.a = 2 * w.a + 2
>>> gt.graph_draw(g, edge_color=w, edge_pen_width=w, vertex_fill_color="grey",
...               output="max_card_match.pdf")
<...>