class_docstr: An implementation of `"FCNMF" <https://arxiv.org/pdf/1804.05313.pdf.>`_
  from the Arxiv '18 paper "Fusing Structure and Content via Non-negative Matrix
  Factorization for Embedding Information Networks". The procedure uses a joint
  matrix factorization technique on the adjacency and feature matrices. The node
  and feature embeddings are co-regularized for alignment of the embedding spaces.
  
  Args:
      dimensions (int): Number of embedding dimensions. Default is 32.
      lower_control (float): Embedding score minimal value. Default is 10**-15.
      iterations (int): Power iterations. Default is 500.
      alpha_1 (float): Alignment parameter for adjacency matrix. Default is 1000.0.
      alpha_2 (float): Adjacency basis regularization. Default is 1.0.
      alpha_3 (float): Adjacency features regularization. Default is 1.0.
      beta_1 (float): Alignment parameter for feature matrix. Default is 1000.0.
      beta_2 (float): Attribute basis regularization. Default is 1.0.
      beta_3 (float): Attribute basis regularization. Default is 1.0.
      seed (int): Random seed value. Default is 42.
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code: def __init__(
      self,
      dimensions: int = 32,
      lower_control: float = 10**-15,
      iterations: int = 500,
      alpha_1: float = 1000.0,
      alpha_2: float = 1.0,
      alpha_3: float = 1.0,
      beta_1: float = 1000.0,
      beta_2: float = 1.0,
      beta_3: float = 1.0,
      seed: int = 42,
  ):
      self.dimensions = dimensions
      self.lower_control = lower_control
      self.iterations = iterations
      self.alpha_1 = alpha_1
      self.alpha_2 = alpha_2
      self.alpha_3 = alpha_3
      self.beta_1 = beta_1
      self.beta_2 = beta_2
      self.beta_3 = beta_3
      self.seed = seed
  
file_path: karateclub/karateclub/node_embedding/attributed/fscnmf.py
imports: import numpy
  import networkx
  from scipy import sparse
  from scipy.sparse import coo_matrix
  from typing import Union
  from numpy.linalg import inv
  from karateclub.estimator import Estimator
label_desc: Use image node_id 1 to create a new FSCNMF object from inherited base classes: Estimator with example: obj = FSCNMF(dimensions, lower_control, iterations, alpha_1, alpha_2, alpha_3, beta_1, beta_2, beta_3, seed)