Warning

This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

Source code for networkx.algorithms.approximation.dominating_set

# -*- coding: utf-8 -*-
#   Copyright (C) 2011-2012 by
#   Nicholas Mancuso <nick.mancuso@gmail.com>
#   All rights reserved.
#   BSD license.
"""Functions for finding node and edge dominating sets.

A *`dominating set`_[1] for an undirected graph *G* with vertex set *V*
and edge set *E* is a subset *D* of *V* such that every vertex not in
*D* is adjacent to at least one member of *D*. An *`edge dominating
set`_[2] is a subset *F* of *E* such that every edge not in *F* is
incident to an endpoint of at least one edge in *F*.

.. [1] dominating set: https://en.wikipedia.org/wiki/Dominating_set
.. [2] edge dominating set: https://en.wikipedia.org/wiki/Edge_dominating_set

"""
from __future__ import division

from ..matching import maximal_matching
from ...utils import not_implemented_for

__all__ = ["min_weighted_dominating_set",
           "min_edge_dominating_set"]

__author__ = """Nicholas Mancuso (nick.mancuso@gmail.com)"""


# TODO Why doesn't this algorithm work for directed graphs?
[docs]@not_implemented_for('directed') def min_weighted_dominating_set(G, weight=None): """Returns a dominating set that approximates the minimum weight node dominating set. Parameters ---------- G : NetworkX graph Undirected graph. weight : string The node attribute storing the weight of an edge. If provided, the node attribute with this key must be a number for each node. If not provided, each node is assumed to have weight one. Returns ------- min_weight_dominating_set : set A set of nodes, the sum of whose weights is no more than `(\log w(V)) w(V^*)`, where `w(V)` denotes the sum of the weights of each node in the graph and `w(V^*)` denotes the sum of the weights of each node in the minimum weight dominating set. Notes ----- This algorithm computes an approximate minimum weighted dominating set for the graph ``G``. The returned solution has weight `(\log w(V)) w(V^*)`, where `w(V)` denotes the sum of the weights of each node in the graph and `w(V^*)` denotes the sum of the weights of each node in the minimum weight dominating set for the graph. This implementation of the algorithm runs in `O(m)` time, where `m` is the number of edges in the graph. References ---------- .. [1] Vazirani, Vijay V. *Approximation Algorithms*. Springer Science & Business Media, 2001. """ # The unique dominating set for the null graph is the empty set. if len(G) == 0: return set() # This is the dominating set that will eventually be returned. dom_set = set() def _cost(node_and_neighborhood): """Returns the cost-effectiveness of greedily choosing the given node. `node_and_neighborhood` is a two-tuple comprising a node and its closed neighborhood. """ v, neighborhood = node_and_neighborhood return G.node[v].get(weight, 1) / len(neighborhood - dom_set) # This is a set of all vertices not already covered by the # dominating set. vertices = set(G) # This is a dictionary mapping each node to the closed neighborhood # of that node. neighborhoods = {v: {v} | set(G[v]) for v in G} # Continue until all vertices are adjacent to some node in the # dominating set. while vertices: # Find the most cost-effective node to add, along with its # closed neighborhood. dom_node, min_set = min(neighborhoods.items(), key=_cost) # Add the node to the dominating set and reduce the remaining # set of nodes to cover. dom_set.add(dom_node) del neighborhoods[dom_node] vertices -= min_set return dom_set
[docs]def min_edge_dominating_set(G): r"""Return minimum cardinality edge dominating set. Parameters ---------- G : NetworkX graph Undirected graph Returns ------- min_edge_dominating_set : set Returns a set of dominating edges whose size is no more than 2 * OPT. Notes ----- The algorithm computes an approximate solution to the edge dominating set problem. The result is no more than 2 * OPT in terms of size of the set. Runtime of the algorithm is `O(|E|)`. """ if not G: raise ValueError("Expected non-empty NetworkX graph!") return maximal_matching(G)