/usr/lib/python2.7/dist-packages/networkx/algorithms/bipartite/spectral.py is in python-networkx 1.8.1-0ubuntu3.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | # -*- coding: utf-8 -*-
"""
Spectral bipartivity measure.
"""
import networkx as nx
__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
# Copyright (C) 2011 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
# All rights reserved.
# BSD license.
__all__ = ['spectral_bipartivity']
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.
See Also
--------
color
References
----------
.. [1] 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 = G.nodes() # 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")
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