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: Using the random orthogonal smoothing.
docstr_tok: ["Using","the","random","orthogonal","smoothing","."]
code: def _create_embedding(self, A_hat):
      """
      Using the random orthogonal smoothing.
      """
      sd = 1 / self.dimensions
      base_embedding = np.random.normal(
          0, sd, (A_hat.shape[0], self.dimensions)
      )
      base_embedding, _ = np.linalg.qr(base_embedding)
      embedding = np.zeros(base_embedding.shape)
      alpha_sum = sum(self.alphas)
      for alpha in self.alphas:
          base_embedding = A_hat.dot(base_embedding)
          embedding = embedding + alpha * base_embedding
      embedding = embedding / alpha_sum
      embedding = (embedding - embedding.mean(0)) / embedding.std(0)
      return embedding
  
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 4 for calling the RandNE obj's underlying member method code with example usage: obj._create_embedding(A_hat) and returns: embedding