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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code: def __init__(
      self,
      dimensions: int = 128,
      alphas: list = [0.5, 0.5],
      seed: int = 42,
  ):
      self.dimensions = dimensions
      self.alphas = alphas
      self.seed = seed
  
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 1 to create a new RandNE object from inherited base classes: Estimator with example: obj = RandNE(dimensions, alphas, seed)