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 __init__(
      self,
      dimensions: int = 128,
      workers: int = 4,
      epochs: int = 100,
      down_sampling: float = 0.0001,
      learning_rate: float = 0.05,
      min_count: int = 1,
      seed: int = 42,
  ):
      self.dimensions = dimensions
      self.workers = workers
      self.epochs = epochs
      self.down_sampling = down_sampling
      self.learning_rate = learning_rate
      self.min_count = min_count
      self.seed = seed
  
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 1 to create a new ASNE object from inherited base classes: Estimator with example: obj = ASNE(dimensions, workers, epochs, down_sampling, learning_rate, min_count, seed)