Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.9/dist-packages/networkx/algorithms/centrality/degree_alg.py: 42%
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« prev ^ index » next coverage.py v7.3.2, created at 2023-10-20 07:00 +0000
1"""Degree centrality measures."""
2import networkx as nx
3from networkx.utils.decorators import not_implemented_for
5__all__ = ["degree_centrality", "in_degree_centrality", "out_degree_centrality"]
8@nx._dispatch
9def degree_centrality(G):
10 """Compute the degree centrality for nodes.
12 The degree centrality for a node v is the fraction of nodes it
13 is connected to.
15 Parameters
16 ----------
17 G : graph
18 A networkx graph
20 Returns
21 -------
22 nodes : dictionary
23 Dictionary of nodes with degree centrality as the value.
25 Examples
26 --------
27 >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
28 >>> nx.degree_centrality(G)
29 {0: 1.0, 1: 1.0, 2: 0.6666666666666666, 3: 0.6666666666666666}
31 See Also
32 --------
33 betweenness_centrality, load_centrality, eigenvector_centrality
35 Notes
36 -----
37 The degree centrality values are normalized by dividing by the maximum
38 possible degree in a simple graph n-1 where n is the number of nodes in G.
40 For multigraphs or graphs with self loops the maximum degree might
41 be higher than n-1 and values of degree centrality greater than 1
42 are possible.
43 """
44 if len(G) <= 1:
45 return {n: 1 for n in G}
47 s = 1.0 / (len(G) - 1.0)
48 centrality = {n: d * s for n, d in G.degree()}
49 return centrality
52@not_implemented_for("undirected")
53@nx._dispatch
54def in_degree_centrality(G):
55 """Compute the in-degree centrality for nodes.
57 The in-degree centrality for a node v is the fraction of nodes its
58 incoming edges are connected to.
60 Parameters
61 ----------
62 G : graph
63 A NetworkX graph
65 Returns
66 -------
67 nodes : dictionary
68 Dictionary of nodes with in-degree centrality as values.
70 Raises
71 ------
72 NetworkXNotImplemented
73 If G is undirected.
75 Examples
76 --------
77 >>> G = nx.DiGraph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
78 >>> nx.in_degree_centrality(G)
79 {0: 0.0, 1: 0.3333333333333333, 2: 0.6666666666666666, 3: 0.6666666666666666}
81 See Also
82 --------
83 degree_centrality, out_degree_centrality
85 Notes
86 -----
87 The degree centrality values are normalized by dividing by the maximum
88 possible degree in a simple graph n-1 where n is the number of nodes in G.
90 For multigraphs or graphs with self loops the maximum degree might
91 be higher than n-1 and values of degree centrality greater than 1
92 are possible.
93 """
94 if len(G) <= 1:
95 return {n: 1 for n in G}
97 s = 1.0 / (len(G) - 1.0)
98 centrality = {n: d * s for n, d in G.in_degree()}
99 return centrality
102@not_implemented_for("undirected")
103@nx._dispatch
104def out_degree_centrality(G):
105 """Compute the out-degree centrality for nodes.
107 The out-degree centrality for a node v is the fraction of nodes its
108 outgoing edges are connected to.
110 Parameters
111 ----------
112 G : graph
113 A NetworkX graph
115 Returns
116 -------
117 nodes : dictionary
118 Dictionary of nodes with out-degree centrality as values.
120 Raises
121 ------
122 NetworkXNotImplemented
123 If G is undirected.
125 Examples
126 --------
127 >>> G = nx.DiGraph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
128 >>> nx.out_degree_centrality(G)
129 {0: 1.0, 1: 0.6666666666666666, 2: 0.0, 3: 0.0}
131 See Also
132 --------
133 degree_centrality, in_degree_centrality
135 Notes
136 -----
137 The degree centrality values are normalized by dividing by the maximum
138 possible degree in a simple graph n-1 where n is the number of nodes in G.
140 For multigraphs or graphs with self loops the maximum degree might
141 be higher than n-1 and values of degree centrality greater than 1
142 are possible.
143 """
144 if len(G) <= 1:
145 return {n: 1 for n in G}
147 s = 1.0 / (len(G) - 1.0)
148 centrality = {n: d * s for n, d in G.out_degree()}
149 return centrality