class_docstr: An implementation of `"GraphWave" <https://dl.acm.org/citation.cfm?id=2806512>`_
  from the KDD '18 paper "Learning Structural Node Embeddings Via Diffusion Wavelets".
  The procedure first calculates the graph wavelets using a heat kernel. The wavelets
  are treated as probability distributions over nodes from a source node. Using these
  the characteristic function is evaluated at certain gird points to learn structural
  node embeddings of the vertices.
  
  Args:
      sample_number (int): Number of evaluation points. Default is 200.
      step_size (float): Grid point step size. Default is 0.1.
      heat_coefficient (float): Heat kernel coefficient. Default is 1.0.
      approximation (int): Chebyshev polynomial order. Default is 100.
      mechanism (str): Wavelet calculation method one of:
                       (:obj:`"exact"`, :obj:`"approximate"`). Default is 'approximate'.
      switch (int): Vertex cardinality when the wavelet calculation method switches to approximation. Default is 1000.
      seed (int): Random seed value. Default is 42.
docstr: Estimating the largest eigenvalue.
  Setting up the heat filter and the Cheybshev polynomial.
  Using the approximate wavelet calculator method.
docstr_tok: ["Estimating","the","largest","eigenvalue",".","Setting","up","the","heat","filter","and","the","Cheybshev","polynomial",".","Using","the","approximate","wavelet","calculator","method","."]
code: def _approximate_structural_wavelet_embedding(self):
      """
      Estimating the largest eigenvalue.
      Setting up the heat filter and the Cheybshev polynomial.
      Using the approximate wavelet calculator method.
      """
      self._G.estimate_lmax()
      self._heat_filter = pygsp.filters.Heat(
          self._G, tau=[self.heat_coefficient]
      )
      self._chebyshev = (
          pygsp.filters.approximations.compute_cheby_coeff(
              self._heat_filter, m=self.approximation
          )
      )
      self._approximate_wavelet_calculator()
  
file_path: karateclub/karateclub/node_embedding/structural/graphwave.py
imports: import pygsp
  import numpy
  import networkx
  from karateclub.estimator import Estimator
label_desc: Use image node_id 8 for calling the GraphWave obj's underlying member method code with example usage: obj._approximate_structural_wavelet_embedding() without return types