class_docstr: An implementation of `"ASNE" <https://arxiv.org/abs/1705.04969>`_
  from the TKDE '18 paper "Attributed Social Network Embedding". The
  procedure implicitly factorizes a concatenated adjacency matrix and feature matrix.
  
  Args:
      dimensions (int): Dimensionality of embedding. Default is 128.
      workers (int): Number of cores. Default is 4.
      epochs (int): Number of epochs. Default is 100.
      down_sampling (float): Down sampling frequency. Default is 0.0001.
      learning_rate (float): HogWild! learning rate. Default is 0.05.
      min_count (int): Minimal count of node occurrences. Default is 1.
      seed (int): Random seed value. Default is 42.
class_docstr_tok: ["An","implementation","of","`","``","ASNE","''","<","https",":","\/\/arxiv.org\/abs\/1705.04969",">","`","_","from","the","TKDE","'18","paper","``","Attributed","Social","Network","Embedding","''",".","The","procedure","implicitly","factorizes","a","concatenated","adjacency","matrix","and","feature","matrix",".","Args",":","dimensions","(","int",")",":","Dimensionality","of","embedding",".","Default","is","128.","workers","(","int",")",":","Number","of","cores",".","Default","is","4.","epochs","(","int",")",":","Number","of","epochs",".","Default","is","100.","down_sampling","(","float",")",":","Down","sampling","frequency",".","Default","is","0.0001.","learning_rate","(","float",")",":","HogWild","!","learning","rate",".","Default","is","0.05.","min_count","(","int",")",":","Minimal","count","of","node","occurrences",".","Default","is","1.","seed","(","int",")",":","Random","seed","value",".","Default","is","42","."]
docstr: Fitting an ASNE model.
  
  Arg types:
      * **graph** *(NetworkX graph)* - The graph to be embedded.
      * **X** *(Scipy COO array)* - The matrix of node features.
docstr_tok: ["Fitting","an","ASNE","model",".","Arg","types",":","*","*","*","graph","*","*","*","(","NetworkX","graph",")","*","-","The","graph","to","be","embedded",".","*","*","*","X","*","*","*","(","Scipy","COO","array",")","*","-","The","matrix","of","node","features","."]
code: def fit(self, graph: nx.classes.graph.Graph, X: coo_matrix):
      """
      Fitting an ASNE model.
  
      Arg types:
          * **graph** *(NetworkX graph)* - The graph to be embedded.
          * **X** *(Scipy COO array)* - The matrix of node features.
      """
      self._set_seed()
      graph = self._check_graph(graph)
      features = self._feature_transform(graph, X)
      documents = [
          TaggedDocument(words=features[node], tags=[str(node)])
          for node in range(len(features))
      ]
  
      model = Doc2Vec(
          documents,
          vector_size=self.dimensions,
          window=0,
          min_count=self.min_count,
          dm=0,
          sample=self.down_sampling,
          workers=self.workers,
          epochs=self.epochs,
          alpha=self.learning_rate,
          seed=self.seed,
      )
  
      self._embedding = np.array(
          [model.dv[str(i)] for i, _ in enumerate(documents)]
      )
  
file_path: karateclub/karateclub/node_embedding/attributed/asne.py
imports: import random
  import numpy
  import networkx
  from scipy.sparse import coo_matrix
  from karateclub.estimator import Estimator
  from gensim.models.doc2vec import Doc2Vec, TaggedDocument
label_desc: Use image node_id 3 for calling the ASNE obj's underlying member method code with example usage: obj.fit(graph, X) without return types