class_docstr: An implementation of `"MUSAE" <https://arxiv.org/abs/1909.13021>`_
  from the Arxiv '19 paper "MUSAE: Multi-Scale Attributed Node Embedding". The
  procedure does attributed random walks to approximate the adjacency matrix power
  node feature matrix products. The matrices are decomposed implicitly by a Skip-Gram
  style optimizer. The individual embeddings are concatenated together to form a
  multi-scale attributed node embedding. This way the feature distributions at different scales
  are separable.
  
  Args:
      walk_number (int): Number of random walks. Default is 5.
      walk_length (int): Length of random walks. Default is 80.
      dimensions (int): Dimensionality of embedding. Default is 32.
      workers (int): Number of cores. Default is 4.
      window_size (int): Matrix power order. Default is 3.
      epochs (int): Number of epochs. Default is 1.
      learning_rate (float): HogWild! learning rate. Default is 0.05.
      down_sampling (float): Down sampling rate in the corpus. Default is 0.0001.
      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,
      walk_number=5,
      walk_length=80,
      dimensions=32,
      workers=4,
      window_size=3,
      epochs=5,
      learning_rate=0.05,
      down_sampling=0.0001,
      min_count=1,
      seed=42,
  ):
      self.walk_number = walk_number
      self.walk_length = walk_length
      self.dimensions = dimensions
      self.workers = workers
      self.window_size = window_size
      self.epochs = epochs
      self.learning_rate = learning_rate
      self.down_sampling = down_sampling
      self.min_count = min_count
      self.seed = seed
  
file_path: karateclub/karateclub/node_embedding/attributed/musae.py
imports: import random
  import numpy
  import networkx
  from typing import Union
  from scipy.sparse import coo_matrix
  from karateclub.estimator import Estimator
  from gensim.models.doc2vec import TaggedDocument, Doc2Vec
  from karateclub.utils.walker import RandomWalker
label_desc: Use image node_id 1 to create a new MUSAE object from inherited base classes: Estimator with example: obj = MUSAE(walk_number, walk_length, dimensions, workers, window_size, epochs, learning_rate, down_sampling, min_count, seed)