class_docstr: An implementation of `"RandNE" <https://zw-zhang.github.io/files/2018_ICDM_RandNE.pdf>`_ from the ICDM '18 paper "Billion-scale Network Embedding with Iterative Random Projection". The procedure uses normalized adjacency matrix based
  smoothing on an orthogonalized random normally generate base node embedding matrix.
  
  Args:
      dimensions (int): Number of embedding dimension. Default is 128.
      alphas (list): Smoothing weights for adjacency matrix powers. Default is [0.5, 0.5].
      seed (int): Random seed. Default is 42.
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docstr: Creating the normalized adjacency matrix.
  
  Arg types:
      * **graph** *(NetworkX graph)* - The graph to be embedded.
  
  Return types:
      * **(A_hat, A_hat, A_hat, D_inverse)** *(SciPy arrays)* - Normalized adjacency matrices.
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code: def _create_smoothing_matrix(self, graph):
      """
      Creating the normalized adjacency matrix.
  
      Arg types:
          * **graph** *(NetworkX graph)* - The graph to be embedded.
  
      Return types:
          * **(A_hat, A_hat, A_hat, D_inverse)** *(SciPy arrays)* - Normalized adjacency matrices.
      """
      A = nx.adjacency_matrix(
          graph, nodelist=range(graph.number_of_nodes())
      )
      D_inverse = self._create_D_inverse(graph)
      A_hat = D_inverse.dot(A)
      return A_hat
  
file_path: karateclub/karateclub/node_embedding/neighbourhood/randne.py
imports: import numpy
  import networkx
  from scipy import sparse
  from karateclub.estimator import Estimator
label_desc: Use image node_id 3 for calling the RandNE obj's underlying member method code with example usage: obj._create_smoothing_matrix(graph) and returns: A_hat