text: """
  Eigenvalue spectrum of graphs.
  """
  #    Copyright (C) 2004-2019 by
  #    Aric Hagberg <hagberg@lanl.gov>
  #    Dan Schult <dschult@colgate.edu>
  #    Pieter Swart <swart@lanl.gov>
  #    All rights reserved.
  #    BSD license.
  import networkx as nx
  __author__ = "\n".join(['Aric Hagberg <aric.hagberg@gmail.com>',
                          'Pieter Swart (swart@lanl.gov)',
                          'Dan Schult(dschult@colgate.edu)',
                          'Jean-Gabriel Young (jean.gabriel.young@gmail.com)'])
  
  __all__ = ['laplacian_spectrum', 'adjacency_spectrum', 'modularity_spectrum',
             'normalized_laplacian_spectrum', 'bethe_hessian_spectrum']
  
  
  def laplacian_spectrum(G, weight='weight'):
      """Returns eigenvalues of the Laplacian of G
  
      Parameters
      ----------
      G : graph
         A NetworkX graph
  
      weight : string or None, optional (default='weight')
         The edge data key used to compute each value in the matrix.
         If None, then each edge has weight 1.
  
      Returns
      -------
      evals : NumPy array
        Eigenvalues
  
      Notes
      -----
      For MultiGraph/MultiDiGraph, the edges weights are summed.
      See to_numpy_matrix for other options.
  
      See Also
      --------
      laplacian_matrix
      """
      from scipy.linalg import eigvalsh
      return eigvalsh(nx.laplacian_matrix(G, weight=weight).todense())
  
  
  def normalized_laplacian_spectrum(G, weight='weight'):
      """Return eigenvalues of the normalized Laplacian of G
  
      Parameters
      ----------
      G : graph
         A NetworkX graph
  
      weight : string or None, optional (default='weight')
         The edge data key used to compute each value in the matrix.
         If None, then each edge has weight 1.
  
      Returns
      -------
      evals : NumPy array
        Eigenvalues
  
      Notes
      -----
      For MultiGraph/MultiDiGraph, the edges weights are summed.
      See to_numpy_matrix for other options.
  
      See Also
      --------
      normalized_laplacian_matrix
      """
      from scipy.linalg import eigvalsh
      return eigvalsh(nx.normalized_laplacian_matrix(G, weight=weight).todense())
  
  
  def adjacency_spectrum(G, weight='weight'):
      """Returns eigenvalues of the adjacency matrix of G.
  
      Parameters
      ----------
      G : graph
         A NetworkX graph
  
      weight : string or None, optional (default='weight')
         The edge data key used to compute each value in the matrix.
         If None, then each edge has weight 1.
  
      Returns
      -------
      evals : NumPy array
        Eigenvalues
  
      Notes
      -----
      For MultiGraph/MultiDiGraph, the edges weights are summed.
      See to_numpy_matrix for other options.
  
      See Also
      --------
      adjacency_matrix
      """
      from scipy.linalg import eigvals
      return eigvals(nx.adjacency_matrix(G, weight=weight).todense())
  
  
  def modularity_spectrum(G):
      """Returns eigenvalues of the modularity matrix of G.
  
      Parameters
      ----------
      G : Graph
         A NetworkX Graph or DiGraph
  
      Returns
      -------
      evals : NumPy array
        Eigenvalues
  
      See Also
      --------
      modularity_matrix
  
      References
      ----------
      .. [1] M. E. J. Newman, "Modularity and community structure in networks",
         Proc. Natl. Acad. Sci. USA, vol. 103, pp. 8577-8582, 2006.
      """
      from scipy.linalg import eigvals
      if G.is_directed():
          return eigvals(nx.directed_modularity_matrix(G))
      else:
          return eigvals(nx.modularity_matrix(G))
  
  
  def bethe_hessian_spectrum(G, r=None):
      """Returns eigenvalues of the Bethe Hessian matrix of G.
  
      Parameters
      ----------
      G : Graph
         A NetworkX Graph or DiGraph
  
      r : float
         Regularizer parameter
  
      Returns
      -------
      evals : NumPy array
        Eigenvalues
  
      See Also
      --------
      bethe_hessian_matrix
  
      References
      ----------
      .. [1] A. Saade, F. Krzakala and L. Zdeborová
         "Spectral clustering of graphs with the bethe hessian",
         Advances in Neural Information Processing Systems. 2014.
      """
      from scipy.linalg import eigvalsh
      return eigvalsh(nx.bethe_hessian_matrix(G, r).todense())
  
  
  # fixture for pytest
  def setup_module(module):
      import pytest
      scipy.linalg = pytest.importorskip('scipy.linalg')
  