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: Anchor sampling procedure.
  
  Arg types:
      * **index** *(int)* - The axis for marginalization.
  
  Return types:
      * **sample** *(int)* - Anchor point index.
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code: def _sampler(self, index):
      """
      Anchor sampling procedure.
  
      Arg types:
          * **index** *(int)* - The axis for marginalization.
  
      Return types:
          * **sample** *(int)* - Anchor point index.
      """
      row_weights = self._residuals.sum(axis=index)
      if len(row_weights.shape) > 1:
          row_weights = row_weights.reshape(-1)
      sums = np.sum(np.sum(row_weights))
      to_pick_from = row_weights.reshape(-1)
      to_pick_from = (to_pick_from / np.sum(to_pick_from)).tolist()[0]
      sample = self._binary_search(to_pick_from)
      return sample
  
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 5 for calling the BoostNE obj's underlying member method code with example usage: obj._sampler(index) and returns: sample