class_docstr: An implementation of `"Symm-NMF" <https://www.cc.gatech.edu/~hpark/papers/DaDingParkSDM12.pdf>`_
  from the SDM'12 paper "Symmetric Nonnegative Matrix Factorization for Graph Clustering". The procedure
  decomposed the second power od the normalized adjacency matrix with an ADMM based non-negative matrix
  factorization based technique. This results in a node embedding and each node is associated with an
  embedding factor in the created latent space.
  
  Args:
      dimensions (int): Number of dimensions. Default is 32.
      iterations (int): Number of power iterations. Default is 200.
      rho (float): Regularization tuning parameter. Default is 100.0.
      seed (int): Random seed value. Default is 42.
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docstr: Setup the node embedding matrices.
  
  Arg types:
      * **graph** *(NetworkX graph)* - The graph to be clustered.
docstr_tok: ["Setup","the","node","embedding","matrices",".","Arg","types",":","*","*","*","graph","*","*","*","(","NetworkX","graph",")","*","-","The","graph","to","be","clustered","."]
code: def _setup_embeddings(self, graph):
      """
      Setup the node embedding matrices.
  
      Arg types:
          * **graph** *(NetworkX graph)* - The graph to be clustered.
      """
      number_of_nodes = graph.shape[0]
      non_zero = graph.nonzero()[0].shape[0]
      self._H = np.random.uniform(
          0,
          non_zero / (number_of_nodes**2),
          size=(number_of_nodes, self.dimensions),
      )
      self._H_gamma = np.zeros((number_of_nodes, self.dimensions))
      self._I = np.identity(self.dimensions)
  
file_path: karateclub/karateclub/community_detection/overlapping/symmnmf.py
imports: import numpy
  import networkx
  from typing import Dict
  from scipy import sparse
  from karateclub.estimator import Estimator
label_desc: Use image node_id 4 for calling the SymmNMF obj's underlying member method code with example usage: obj._setup_embeddings(graph) without return types