Warning

This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

# Source code for networkx.algorithms.bipartite.spectral

# -*- coding: utf-8 -*-
"""
Spectral bipartivity measure.
"""
import networkx as nx
__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
__all__ = ['spectral_bipartivity']

[docs]def spectral_bipartivity(G, nodes=None, weight='weight'):
"""Returns the spectral bipartivity.

Parameters
----------
G : NetworkX graph

nodes : list or container  optional(default is all nodes)
Nodes to return value of spectral bipartivity contribution.

weight : string or None  optional (default = 'weight')
Edge data key to use for edge weights. If None, weights set to 1.

Returns
-------
sb : float or dict
A single number if the keyword nodes is not specified, or
a dictionary keyed by node with the spectral bipartivity contribution
of that node as the value.

Examples
--------
>>> from networkx.algorithms import bipartite
>>> G = nx.path_graph(4)
>>> bipartite.spectral_bipartivity(G)
1.0

Notes
-----
This implementation uses Numpy (dense) matrices which are not efficient
for storing large sparse graphs.

--------
color

References
----------
..  E. Estrada and J. A. Rodríguez-Velázquez, "Spectral measures of
bipartivity in complex networks", PhysRev E 72, 046105 (2005)
"""
try:
import scipy.linalg
except ImportError:
raise ImportError('spectral_bipartivity() requires SciPy: ',
'http://scipy.org/')
nodelist = list(G)  # ordering of nodes in matrix
A = nx.to_numpy_matrix(G, nodelist, weight=weight)
expA = scipy.linalg.expm(A)
expmA = scipy.linalg.expm(-A)
coshA = 0.5 * (expA + expmA)
if nodes is None:
# return single number for entire graph
return coshA.diagonal().sum() / expA.diagonal().sum()
else:
# contribution for individual nodes
index = dict(zip(nodelist, range(len(nodelist))))
sb = {}
for n in nodes:
i = index[n]
sb[n] = coshA[i, i] / expA[i, i]
return sb

def setup_module(module):
"""Fixture for nose tests."""
from nose import SkipTest
try:
import numpy
except:
raise SkipTest("NumPy not available")
try:
import scipy
except:
raise SkipTest("SciPy not available")