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.
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code: def _feature_transform(self, graph, X):
      features = {
          node: ["neb_" + str(neb) for neb in graph.neighbors(node)]
          for node in graph.nodes()
      }
      nodes = X.row
      for i, node in enumerate(nodes):
          features[node].append("feature_" + str(X.col[i]))
      return features
  
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 2 for calling the ASNE obj's underlying member method code with example usage: obj._feature_transform(graph, X) and returns: features