class_docstr: An implementation of `"TADW" <https://www.ijcai.org/Proceedings/15/Papers/299.pdf>`_
  from the IJCAI '15 paper "Network Representation Learning with Rich Text Information". The
  procedure uses the node attribute matrix with a factorization matrix to reproduce a power
  of the adjacency matrix to create representations.
  
  Args:
      dimensions (int): Number of embedding dimensions. Default is 32.
      reduction_dimensions (int): SVD reduction dimensions. Default is 64.
      svd_iterations (int): SVD iteration count. Default is 20.
      seed (int): Random seed. Default is 42.
      alpha (float): Learning rate. Default is 0.01.
      iterations (int): Matrix decomposition iterations. Default is 10.
      lambd (float): Regularization coefficient. Default is 10.0.
docstr: Creating a normalized sparse adjacency matrix power target.
  
  Arg types:
      * **graph** *(NetworkX graph)* - The graph to be embedded.
  
  Return types:
      * **A_tilde** *(Scipy COO matrix) - The target matrix.
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code: def _create_target_matrix(self, graph):
      """
      Creating a normalized sparse adjacency matrix power target.
  
      Arg types:
          * **graph** *(NetworkX graph)* - The graph to be embedded.
  
      Return types:
          * **A_tilde** *(Scipy COO matrix) - The target matrix.
      """
      weighted_graph = nx.Graph()
      for u, v in graph.edges():
          weighted_graph.add_edge(u, v, weight=1.0 / graph.degree(u))
          weighted_graph.add_edge(v, u, weight=1.0 / graph.degree(v))
      A_hat = nx.adjacency_matrix(
          weighted_graph, nodelist=range(graph.number_of_nodes())
      )
  
      A_tilde = A_hat.dot(A_hat)
      return coo_matrix(A_tilde)
  
file_path: karateclub/karateclub/node_embedding/attributed/tadw.py
imports: import numpy
  import networkx
  from typing import Union
  from scipy.sparse import coo_matrix
  from karateclub.estimator import Estimator
  from sklearn.decomposition import TruncatedSVD
label_desc: Use image node_id 2 for calling the TADW obj's underlying member method code with example usage: obj._create_target_matrix(graph) and returns: coo_matrix