Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.9/dist-packages/networkx/algorithms/vitality.py: 42%

1""" 

2Vitality measures. 

3""" 

4from functools import partial 

5 

6import networkx as nx 

7 

8__all__ = ["closeness_vitality"] 

9 

10 

11@nx._dispatch(edge_attrs="weight") 

12def closeness_vitality(G, node=None, weight=None, wiener_index=None): 

13 """Returns the closeness vitality for nodes in the graph. 

14 

15 The *closeness vitality* of a node, defined in Section 3.6.2 of [1], 

16 is the change in the sum of distances between all node pairs when 

17 excluding that node. 

18 

19 Parameters 

20 ---------- 

21 G : NetworkX graph 

22 A strongly-connected graph. 

23 

24 weight : string 

25 The name of the edge attribute used as weight. This is passed 

26 directly to the :func:`~networkx.wiener_index` function. 

27 

28 node : object 

29 If specified, only the closeness vitality for this node will be 

30 returned. Otherwise, a dictionary mapping each node to its 

31 closeness vitality will be returned. 

32 

33 Other parameters 

34 ---------------- 

35 wiener_index : number 

36 If you have already computed the Wiener index of the graph 

37 `G`, you can provide that value here. Otherwise, it will be 

38 computed for you. 

39 

40 Returns 

41 ------- 

42 dictionary or float 

43 If `node` is None, this function returns a dictionary 

44 with nodes as keys and closeness vitality as the 

45 value. Otherwise, it returns only the closeness vitality for the 

46 specified `node`. 

47 

48 The closeness vitality of a node may be negative infinity if 

49 removing that node would disconnect the graph. 

50 

51 Examples 

52 -------- 

53 >>> G = nx.cycle_graph(3) 

54 >>> nx.closeness_vitality(G) 

55 {0: 2.0, 1: 2.0, 2: 2.0} 

56 

57 See Also 

58 -------- 

59 closeness_centrality 

60 

61 References 

62 ---------- 

63 .. [1] Ulrik Brandes, Thomas Erlebach (eds.). 

64 *Network Analysis: Methodological Foundations*. 

65 Springer, 2005. 

66 <http://books.google.com/books?id=TTNhSm7HYrIC> 

67 

68 """ 

69 if wiener_index is None: 

70 wiener_index = nx.wiener_index(G, weight=weight) 

71 if node is not None: 

72 after = nx.wiener_index(G.subgraph(set(G) - {node}), weight=weight) 

73 return wiener_index - after 

74 vitality = partial(closeness_vitality, G, weight=weight, wiener_index=wiener_index) 

75 # TODO This can be trivially parallelized. 

76 return {v: vitality(node=v) for v in G}