1""" 2Algorithm to find a maximal (not maximum) independent set. 3 4""" 5import networkx as nx 6from networkx.utils import not_implemented_for 7from networkx.utils import py_random_state 8 9__all__ = ["maximal_independent_set"] 10 11 12@py_random_state(2) 13@not_implemented_for("directed") 14def maximal_independent_set(G, nodes=None, seed=None): 15 """Returns a random maximal independent set guaranteed to contain 16 a given set of nodes. 17 18 An independent set is a set of nodes such that the subgraph 19 of G induced by these nodes contains no edges. A maximal 20 independent set is an independent set such that it is not possible 21 to add a new node and still get an independent set. 22 23 Parameters 24 ---------- 25 G : NetworkX graph 26 27 nodes : list or iterable 28 Nodes that must be part of the independent set. This set of nodes 29 must be independent. 30 31 seed : integer, random_state, or None (default) 32 Indicator of random number generation state. 33 See :ref:`Randomness<randomness>`. 34 35 Returns 36 ------- 37 indep_nodes : list 38 List of nodes that are part of a maximal independent set. 39 40 Raises 41 ------ 42 NetworkXUnfeasible 43 If the nodes in the provided list are not part of the graph or 44 do not form an independent set, an exception is raised. 45 46 NetworkXNotImplemented 47 If `G` is directed. 48 49 Examples 50 -------- 51 >>> G = nx.path_graph(5) 52 >>> nx.maximal_independent_set(G) # doctest: +SKIP 53 [4, 0, 2] 54 >>> nx.maximal_independent_set(G, [1]) # doctest: +SKIP 55 [1, 3] 56 57 Notes 58 ----- 59 This algorithm does not solve the maximum independent set problem. 60 61 """ 62 if not nodes: 63 nodes = {seed.choice(list(G))} 64 else: 65 nodes = set(nodes) 66 if not nodes.issubset(G): 67 raise nx.NetworkXUnfeasible(f"{nodes} is not a subset of the nodes of G") 68 neighbors = set.union(*[set(G.adj[v]) for v in nodes]) 69 if set.intersection(neighbors, nodes): 70 raise nx.NetworkXUnfeasible(f"{nodes} is not an independent set of G") 71 indep_nodes = list(nodes) 72 available_nodes = set(G.nodes()).difference(neighbors.union(nodes)) 73 while available_nodes: 74 node = seed.choice(list(available_nodes)) 75 indep_nodes.append(node) 76 available_nodes.difference_update(list(G.adj[node]) + [node]) 77 return indep_nodes 78