#-*- coding: utf-8 -*-
"""Node assortativity coefficients and correlation measures.
"""
import networkx as nx
from networkx.algorithms.assortativity.mixing import degree_mixing_matrix, \
attribute_mixing_matrix, numeric_mixing_matrix
from networkx.algorithms.assortativity.pairs import node_degree_xy, \
node_attribute_xy
__author__ = ' '.join(['Aric Hagberg <aric.hagberg@gmail.com>',
'Oleguer Sagarra <oleguer.sagarra@gmail.com>'])
__all__ = ['degree_pearson_correlation_coefficient',
'degree_assortativity_coefficient',
'attribute_assortativity_coefficient',
'numeric_assortativity_coefficient']
[docs]def degree_assortativity_coefficient(G, x='out', y='in', weight=None,
nodes=None):
"""Compute degree assortativity of graph.
Assortativity measures the similarity of connections
in the graph with respect to the node degree.
Parameters
----------
G : NetworkX graph
x: string ('in','out')
The degree type for source node (directed graphs only).
y: string ('in','out')
The degree type for target node (directed graphs only).
weight: string or None, optional (default=None)
The edge attribute that holds the numerical value used
as a weight. If None, then each edge has weight 1.
The degree is the sum of the edge weights adjacent to the node.
nodes: list or iterable (optional)
Compute degree assortativity only for nodes in container.
The default is all nodes.
Returns
-------
r : float
Assortativity of graph by degree.
Examples
--------
>>> G=nx.path_graph(4)
>>> r=nx.degree_assortativity_coefficient(G)
>>> print("%3.1f"%r)
-0.5
See Also
--------
attribute_assortativity_coefficient
numeric_assortativity_coefficient
neighbor_connectivity
degree_mixing_dict
degree_mixing_matrix
Notes
-----
This computes Eq. (21) in Ref. [1]_ , where e is the joint
probability distribution (mixing matrix) of the degrees. If G is
directed than the matrix e is the joint probability of the
user-specified degree type for the source and target.
References
----------
.. [1] M. E. J. Newman, Mixing patterns in networks,
Physical Review E, 67 026126, 2003
.. [2] Foster, J.G., Foster, D.V., Grassberger, P. & Paczuski, M.
Edge direction and the structure of networks, PNAS 107, 10815-20 (2010).
"""
M = degree_mixing_matrix(G, x=x, y=y, nodes=nodes, weight=weight)
return numeric_ac(M)
[docs]def degree_pearson_correlation_coefficient(G, x='out', y='in',
weight=None, nodes=None):
"""Compute degree assortativity of graph.
Assortativity measures the similarity of connections
in the graph with respect to the node degree.
This is the same as degree_assortativity_coefficient but uses the
potentially faster scipy.stats.pearsonr function.
Parameters
----------
G : NetworkX graph
x: string ('in','out')
The degree type for source node (directed graphs only).
y: string ('in','out')
The degree type for target node (directed graphs only).
weight: string or None, optional (default=None)
The edge attribute that holds the numerical value used
as a weight. If None, then each edge has weight 1.
The degree is the sum of the edge weights adjacent to the node.
nodes: list or iterable (optional)
Compute pearson correlation of degrees only for specified nodes.
The default is all nodes.
Returns
-------
r : float
Assortativity of graph by degree.
Examples
--------
>>> G=nx.path_graph(4)
>>> r=nx.degree_pearson_correlation_coefficient(G)
>>> print("%3.1f"%r)
-0.5
Notes
-----
This calls scipy.stats.pearsonr.
References
----------
.. [1] M. E. J. Newman, Mixing patterns in networks
Physical Review E, 67 026126, 2003
.. [2] Foster, J.G., Foster, D.V., Grassberger, P. & Paczuski, M.
Edge direction and the structure of networks, PNAS 107, 10815-20 (2010).
"""
try:
import scipy.stats as stats
except ImportError:
raise ImportError(
"Assortativity requires SciPy: http://scipy.org/ ")
xy=node_degree_xy(G, x=x, y=y, nodes=nodes, weight=weight)
x,y=zip(*xy)
return stats.pearsonr(x,y)[0]
[docs]def attribute_assortativity_coefficient(G,attribute,nodes=None):
"""Compute assortativity for node attributes.
Assortativity measures the similarity of connections
in the graph with respect to the given attribute.
Parameters
----------
G : NetworkX graph
attribute : string
Node attribute key
nodes: list or iterable (optional)
Compute attribute assortativity for nodes in container.
The default is all nodes.
Returns
-------
r: float
Assortativity of graph for given attribute
Examples
--------
>>> G=nx.Graph()
>>> G.add_nodes_from([0,1],color='red')
>>> G.add_nodes_from([2,3],color='blue')
>>> G.add_edges_from([(0,1),(2,3)])
>>> print(nx.attribute_assortativity_coefficient(G,'color'))
1.0
Notes
-----
This computes Eq. (2) in Ref. [1]_ , trace(M)-sum(M))/(1-sum(M),
where M is the joint probability distribution (mixing matrix)
of the specified attribute.
References
----------
.. [1] M. E. J. Newman, Mixing patterns in networks,
Physical Review E, 67 026126, 2003
"""
M = attribute_mixing_matrix(G,attribute,nodes)
return attribute_ac(M)
[docs]def numeric_assortativity_coefficient(G, attribute, nodes=None):
"""Compute assortativity for numerical node attributes.
Assortativity measures the similarity of connections
in the graph with respect to the given numeric attribute.
Parameters
----------
G : NetworkX graph
attribute : string
Node attribute key
nodes: list or iterable (optional)
Compute numeric assortativity only for attributes of nodes in
container. The default is all nodes.
Returns
-------
r: float
Assortativity of graph for given attribute
Examples
--------
>>> G=nx.Graph()
>>> G.add_nodes_from([0,1],size=2)
>>> G.add_nodes_from([2,3],size=3)
>>> G.add_edges_from([(0,1),(2,3)])
>>> print(nx.numeric_assortativity_coefficient(G,'size'))
1.0
Notes
-----
This computes Eq. (21) in Ref. [1]_ , for the mixing matrix of
of the specified attribute.
References
----------
.. [1] M. E. J. Newman, Mixing patterns in networks
Physical Review E, 67 026126, 2003
"""
a = numeric_mixing_matrix(G,attribute,nodes)
return numeric_ac(a)
def attribute_ac(M):
"""Compute assortativity for attribute matrix M.
Parameters
----------
M : numpy array or matrix
Attribute mixing matrix.
Notes
-----
This computes Eq. (2) in Ref. [1]_ , (trace(e)-sum(e))/(1-sum(e)),
where e is the joint probability distribution (mixing matrix)
of the specified attribute.
References
----------
.. [1] M. E. J. Newman, Mixing patterns in networks,
Physical Review E, 67 026126, 2003
"""
try:
import numpy
except ImportError:
raise ImportError(
"attribute_assortativity requires NumPy: http://scipy.org/ ")
if M.sum() != 1.0:
M=M/float(M.sum())
M=numpy.asmatrix(M)
s=(M*M).sum()
t=M.trace()
r=(t-s)/(1-s)
return float(r)
def numeric_ac(M):
# M is a numpy matrix or array
# numeric assortativity coefficient, pearsonr
try:
import numpy
except ImportError:
raise ImportError('numeric_assortativity requires ',
'NumPy: http://scipy.org/')
if M.sum() != 1.0:
M=M/float(M.sum())
nx,ny=M.shape # nx=ny
x=numpy.arange(nx)
y=numpy.arange(ny)
a=M.sum(axis=0)
b=M.sum(axis=1)
vara=(a*x**2).sum()-((a*x).sum())**2
varb=(b*x**2).sum()-((b*x).sum())**2
xy=numpy.outer(x,y)
ab=numpy.outer(a,b)
return (xy*(M-ab)).sum()/numpy.sqrt(vara*varb)
# fixture for nose tests
def setup_module(module):
from nose import SkipTest
try:
import numpy
except:
raise SkipTest("NumPy not available")
try:
import scipy
except:
raise SkipTest("SciPy not available")