class_docstr: An implementation of `"BoostNE" <https://arxiv.org/abs/1808.08627>`_
  from the ASONAM '19 paper "Multi-Level Network Embedding with Boosted Low-Rank
  Matrix Approximation". The procedure uses non-negative matrix factorization
  iteratively to decompose the residuals obtained by previous factorization models.
  The base target matrix is a pooled sum of adjacency matrix powers.
  
  Args:
      dimensions (int): Number of individual embedding dimensions. Default is 8.
      iterations (int): Number of boosting iterations. Default is 16.
      order (int): Number of adjacency matrix powers. Default is 2.
      alpha (float): NMF regularization parameter. Default is 0.01.
      seed (int): Random seed value. Default is 42.
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docstr: Re-scaling the target matrix with the anchor row and column.
  
  Arg types:
      * **X** *(COO Scipy matrix)* - The target matrix.
      * **chosen_row** *(int)* - The row anchor.
      * **choswen_col** *(int)* - The column anchor.
  
  Return types:
      * **X** *(COO Scipy matrix)* - The rescaled target matrix.
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code: def _reweighting(self, X, chosen_row, chosen_col):
      """
      Re-scaling the target matrix with the anchor row and column.
  
      Arg types:
          * **X** *(COO Scipy matrix)* - The target matrix.
          * **chosen_row** *(int)* - The row anchor.
          * **choswen_col** *(int)* - The column anchor.
  
      Return types:
          * **X** *(COO Scipy matrix)* - The rescaled target matrix.
      """
      row_sims = X.dot(chosen_row.transpose())
      column_sims = chosen_col.transpose().dot(X)
      X = sparse.csr_matrix(row_sims).multiply(X)
      X = X.multiply(sparse.csr_matrix(column_sims))
      return X
  
imports: import numpy
  import networkx
  from scipy import sparse
  from sklearn.decomposition import NMF
  from karateclub.estimator import Estimator
  from inspect import signature
label_desc: Use image node_id 6 for calling the BoostNE obj's underlying member method code with example usage: obj._reweighting(X, chosen_row, chosen_col) and returns: X