def attribute_mixing_dict(G, attribute, nodes=None, normalized=False):
"""Returns dictionary representation of mixing matrix for attribute.
Parameters
----------
G : graph
NetworkX graph object.
attribute : string
Node attribute key.
nodes: list or iterable (optional)
Unse nodes in container to build the dict. The default is all nodes.
normalized : bool (default=False)
Return counts if False or probabilities if True.
Examples
--------
>>> G=nx.Graph()
>>> G.add_nodes_from([0,1],color='red')
>>> G.add_nodes_from([2,3],color='blue')
>>> G.add_edge(1,3)
>>> d=nx.attribute_mixing_dict(G,'color')
>>> print(d['red']['blue'])
1
>>> print(d['blue']['red']) # d symmetric for undirected graphs
1
Returns
-------
d : dictionary
Counts or joint probability of occurrence of attribute pairs.
"""
xy_iter = node_attribute_xy(G, attribute, nodes)
return mixing_dict(xy_iter, normalized=normalized)
def attribute_mixing_dict(G, attribute, nodes=None, normalized=False):
"""Return dictionary representation of mixing matrix for attribute.
Parameters
----------
G : graph
NetworkX graph object.
attribute : string
Node attribute key.
nodes: list or iterable (optional)
Unse nodes in container to build the dict. The default is all nodes.
normalized : bool (default=False)
Return counts if False or probabilities if True.
Examples
--------
>>> G=nx.Graph()
>>> G.add_nodes_from([0,1],color='red')
>>> G.add_nodes_from([2,3],color='blue')
>>> G.add_edge(1,3)
>>> d=nx.attribute_mixing_dict(G,'color')
>>> print(d['red']['blue'])
1
>>> print(d['blue']['red']) # d symmetric for undirected graphs
1
Returns
-------
d : dictionary
Counts or joint probability of occurrence of attribute pairs.
"""
xy_iter = node_attribute_xy(G, attribute, nodes)
return mixing_dict(xy_iter, normalized=normalized)
def attribute_mixing_dict(G, attribute, nodes=None, normalized=False):
xy_iter = node_attribute_xy(G, attribute, nodes)
return mixing_dict(xy_iter, normalized=normalized)
