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 dense reduced node feature matrix.
  
  Arg types:
      * **X** *(Scipy COO or Numpy array)* - The wide feature matrix.
  
  Return types:
      * **T** *(Numpy array)* - The reduced feature matrix of nodes.
docstr_tok: ["Creating","a","dense","reduced","node","feature","matrix",".","Arg","types",":","*","*","*","X","*","*","*","(","Scipy","COO","or","Numpy","array",")","*","-","The","wide","feature","matrix",".","Return","types",":","*","*","*","T","*","*","*","(","Numpy","array",")","*","-","The","reduced","feature","matrix","of","nodes","."]
code: def _create_reduced_features(self, X):
      """
      Creating a dense reduced node feature matrix.
  
      Arg types:
          * **X** *(Scipy COO or Numpy array)* - The wide feature matrix.
  
      Return types:
          * **T** *(Numpy array)* - The reduced feature matrix of nodes.
      """
      svd = TruncatedSVD(
          n_components=self.reduction_dimensions,
          n_iter=self.svd_iterations,
          random_state=self.seed,
      )
      svd.fit(X)
      T = svd.transform(X)
      return T.T
  
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 6 for calling the TADW obj's underlying member method code with example usage: obj._create_reduced_features(X) and returns: T