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Source code for networkx.algorithms.approximation.dominating_set

# -*- coding: utf-8 -*-
"""
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Minimum Vertex and Edge Dominating Set
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A dominating set for a graph G = (V, E) is a subset D of V such that every
vertex not in D is joined to at least one member of D by some edge. The
domination number gamma(G) is the number of vertices in a smallest dominating
set for G. Given a graph G = (V, E) find a minimum weight dominating set V'.

http://en.wikipedia.org/wiki/Dominating_set

An edge dominating set for a graph G = (V, E) is a subset D of E such that
every edge not in D is adjacent to at least one edge in D.

http://en.wikipedia.org/wiki/Edge_dominating_set
"""
[docs]def min_weighted_dominating_set(G, weight=None): r"""Return minimum weight vertex dominating set. Parameters ---------- G : NetworkX graph Undirected graph weight : None or string, optional (default = None) If None, every edge has weight/distance/weight 1. If a string, use this edge attribute as the edge weight. Any edge attribute not present defaults to 1. Returns ------- min_weight_dominating_set : set Returns a set of vertices whose weight sum is no more than log w(V) * OPT Notes ----- This algorithm computes an approximate minimum weighted dominating set for the graph G. The upper-bound on the size of the solution is log w(V) * OPT. Runtime of the algorithm is O(|E|). References ---------- ..  Vazirani, Vijay Approximation Algorithms (2001) """ if not G: raise ValueError("Expected non-empty NetworkX graph!") # min cover = min dominating set dom_set = set([]) cost_func = dict((node, nd.get(weight, 1)) \ for node, nd in G.nodes_iter(data=True)) vertices = set(G) sets = dict((node, set([node]) | set(G[node])) for node in G) def _cost(subset): """ Our cost effectiveness function for sets given its weight """ cost = sum(cost_func[node] for node in subset) return cost / float(len(subset - dom_set)) while vertices: # find the most cost effective set, and the vertex that for that set dom_node, min_set = min(sets.items(), key=lambda x: (x, _cost(x))) alpha = _cost(min_set) # reduce the cost for the rest for node in min_set - dom_set: cost_func[node] = alpha # add the node to the dominating set and reduce what we must cover dom_set.add(dom_node) del sets[dom_node] vertices = 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 nx.maximal_matching(G)