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: Fitting a NetMF model.
  
  Arg types:
      * **graph** *(NetworkX graph)* - The graph to be embedded.
docstr_tok: ["Fitting","a","NetMF","model",".","Arg","types",":","*","*","*","graph","*","*","*","(","NetworkX","graph",")","*","-","The","graph","to","be","embedded","."]
code: def fit(self, graph: nx.classes.graph.Graph):
      """
      Fitting a NetMF model.
  
      Arg types:
          * **graph** *(NetworkX graph)* - The graph to be embedded.
      """
      self._set_seed()
      graph = self._check_graph(graph)
      A_hat = self._create_smoothing_matrix(graph)
      self._embedding = self._create_embedding(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 5 for calling the RandNE obj's underlying member method code with example usage: obj.fit(graph) without return types