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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code: def __init__(
      self,
      dimensions: int = 8,
      iterations: int = 16,
      order: int = 2,
      alpha: float = 0.01,
      seed: int = 42,
  ):
      self.dimensions = dimensions
      self.iterations = iterations
      self.order = order
      self.alpha = alpha
      self.seed = seed
  
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 1 to create a new BoostNE object from inherited base classes: Estimator with example: obj = BoostNE(dimensions, iterations, order, alpha, seed)