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.
code: def __init__(
      self,
      dimensions: int = 32,
      reduction_dimensions: int = 64,
      svd_iterations: int = 20,
      seed: int = 42,
      alpha: float = 0.01,
      iterations: int = 10,
      lambd: float = 10.0,
  ):
      self.dimensions = dimensions
      self.reduction_dimensions = reduction_dimensions
      self.svd_iterations = svd_iterations
      self.seed = seed
      self.alpha = alpha
      self.iterations = iterations
      self.lambd = lambd
      self.seed = seed
  
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 1 to create a new TADW object from inherited base classes: Estimator with example: obj = TADW(dimensions, reduction_dimensions, svd_iterations, seed, alpha, iterations, lambd)