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.
The numeric attribute must be an integer.
Parameters
----------
G : NetworkX graph
attribute : string
Node attribute key. The corresponding attribute value must be an
integer.
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
"""
a = numeric_mixing_matrix(G, attribute, nodes)
return numeric_ac(a)
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.
The numeric attribute must be an integer.
Parameters
----------
G : NetworkX graph
attribute : string
Node attribute key. The corresponding attribute value must be an
integer.
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)