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: Fitting a TADW model.
  
  Arg types:
      * **graph** *(NetworkX graph)* - The graph to be embedded.
      * **X** *(Scipy COO or Numpy array)* - The matrix of node features.
docstr_tok: ["Fitting","a","TADW","model",".","Arg","types",":","*","*","*","graph","*","*","*","(","NetworkX","graph",")","*","-","The","graph","to","be","embedded",".","*","*","*","X","*","*","*","(","Scipy","COO","or","Numpy","array",")","*","-","The","matrix","of","node","features","."]
code: def fit(
      self,
      graph: nx.classes.graph.Graph,
      X: Union[np.array, coo_matrix],
  ):
      """
      Fitting a TADW model.
  
      Arg types:
          * **graph** *(NetworkX graph)* - The graph to be embedded.
          * **X** *(Scipy COO or Numpy array)* - The matrix of node features.
      """
      self._set_seed()
      graph = self._check_graph(graph)
      self._A = self._create_target_matrix(graph)
      self._T = self._create_reduced_features(X)
      self._init_weights()
      for _ in range(self.iterations):
          self._update_W()
          self._update_H()
  
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 7 for calling the TADW obj's underlying member method code with example usage: obj.fit(graph, X) without return types