1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
|
"""Modularity matrix of graphs.
"""
import networkx as nx
from networkx.utils import not_implemented_for
__all__ = ["modularity_matrix", "directed_modularity_matrix"]
@not_implemented_for("directed")
@not_implemented_for("multigraph")
def modularity_matrix(G, nodelist=None, weight=None):
r"""Returns the modularity matrix of G.
The modularity matrix is the matrix B = A - <A>, where A is the adjacency
matrix and <A> is the average adjacency matrix, assuming that the graph
is described by the configuration model.
More specifically, the element B_ij of B is defined as
.. math::
A_{ij} - {k_i k_j \over 2 m}
where k_i is the degree of node i, and where m is the number of edges
in the graph. When weight is set to a name of an attribute edge, Aij, k_i,
k_j and m are computed using its value.
Parameters
----------
G : Graph
A NetworkX graph
nodelist : list, optional
The rows and columns are ordered according to the nodes in nodelist.
If nodelist is None, then the ordering is produced by G.nodes().
weight : string or None, optional (default=None)
The edge attribute that holds the numerical value used for
the edge weight. If None then all edge weights are 1.
Returns
-------
B : Numpy matrix
The modularity matrix of G.
Examples
--------
>>> k = [3, 2, 2, 1, 0]
>>> G = nx.havel_hakimi_graph(k)
>>> B = nx.modularity_matrix(G)
See Also
--------
to_numpy_array
modularity_spectrum
adjacency_matrix
directed_modularity_matrix
References
----------
.. [1] M. E. J. Newman, "Modularity and community structure in networks",
Proc. Natl. Acad. Sci. USA, vol. 103, pp. 8577-8582, 2006.
"""
if nodelist is None:
nodelist = list(G)
A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight, format="csr")
k = A.sum(axis=1)
m = k.sum() * 0.5
# Expected adjacency matrix
X = k * k.transpose() / (2 * m)
return A - X
@not_implemented_for("undirected")
@not_implemented_for("multigraph")
def directed_modularity_matrix(G, nodelist=None, weight=None):
"""Returns the directed modularity matrix of G.
The modularity matrix is the matrix B = A - <A>, where A is the adjacency
matrix and <A> is the expected adjacency matrix, assuming that the graph
is described by the configuration model.
More specifically, the element B_ij of B is defined as
.. math::
B_{ij} = A_{ij} - k_i^{out} k_j^{in} / m
where :math:`k_i^{in}` is the in degree of node i, and :math:`k_j^{out}` is the out degree
of node j, with m the number of edges in the graph. When weight is set
to a name of an attribute edge, Aij, k_i, k_j and m are computed using
its value.
Parameters
----------
G : DiGraph
A NetworkX DiGraph
nodelist : list, optional
The rows and columns are ordered according to the nodes in nodelist.
If nodelist is None, then the ordering is produced by G.nodes().
weight : string or None, optional (default=None)
The edge attribute that holds the numerical value used for
the edge weight. If None then all edge weights are 1.
Returns
-------
B : Numpy matrix
The modularity matrix of G.
Examples
--------
>>> G = nx.DiGraph()
>>> G.add_edges_from(
... (
... (1, 2),
... (1, 3),
... (3, 1),
... (3, 2),
... (3, 5),
... (4, 5),
... (4, 6),
... (5, 4),
... (5, 6),
... (6, 4),
... )
... )
>>> B = nx.directed_modularity_matrix(G)
Notes
-----
NetworkX defines the element A_ij of the adjacency matrix as 1 if there
is a link going from node i to node j. Leicht and Newman use the opposite
definition. This explains the different expression for B_ij.
See Also
--------
to_numpy_array
modularity_spectrum
adjacency_matrix
modularity_matrix
References
----------
.. [1] E. A. Leicht, M. E. J. Newman,
"Community structure in directed networks",
Phys. Rev Lett., vol. 100, no. 11, p. 118703, 2008.
"""
if nodelist is None:
nodelist = list(G)
A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight, format="csr")
k_in = A.sum(axis=0)
k_out = A.sum(axis=1)
m = k_in.sum()
# Expected adjacency matrix
X = k_out * k_in / m
return A - X
|