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Source code for networkx.algorithms.bipartite.spectral

# -*- 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']

[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. 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")