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 the specified attribute.
References
----------
.. [1] M. E. J. Newman, Mixing patterns in networks
Physical Review E, 67 026126, 2003
"""
if nodes is None:
nodes = G.nodes
vals = set(G.nodes[n][attribute] for n in nodes)
mapping = {d: i for i, d, in enumerate(vals)}
M = attribute_mixing_matrix(G, attribute, nodes, mapping)
return numeric_ac(M, mapping)
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)
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)
def mixing_matrix(G,crimen=crimen):
from networkx.algorithms.assortativity.mixing import attribute_mixing_matrix
m={x:i for x,i in zip(letras[:crimen],range(crimen))}
return attribute_mixing_matrix(G=G,attribute="crime",mapping=m)
