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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docstr: Fitting an FSCNMF model.
  
  Arg types:
      * **graph** *(NetworkX graph)* - The graph to be embedded.
      * **X** *(Scipy COO or Numpy array)* - The matrix of node features.
docstr_tok: ["Fitting","an","FSCNMF","model",".","Arg","types",":","*","*","*","graph","*","*","*","(","NetworkX","graph",")","*","-","The","graph","to","be","embedded",".","*","*","*","X","*","*","*","(","Scipy","COO","or","Numpy","array",")","*","-","The","matrix","of","node","features","."]
code: def fit(
      self,
      graph: nx.classes.graph.Graph,
      X: Union[np.array, coo_matrix],
  ):
      """
      Fitting an FSCNMF model.
  
      Arg types:
          * **graph** *(NetworkX graph)* - The graph to be embedded.
          * **X** *(Scipy COO or Numpy array)* - The matrix of node features.
      """
      self._set_seed()
      graph = self._check_graph(graph)
      self._X = X
      self._A = self._create_base_matrix(graph)
      self._init_weights()
      for _ in range(self.iterations):
          self._update_B1()
          self._update_B2()
          self._update_U()
          self._update_V()
  
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 9 for calling the FSCNMF obj's underlying member method code with example usage: obj.fit(graph, X) without return types