class_docstr: An implementation of `"SINE" <https://arxiv.org/pdf/1810.06768.pdf>`_
  from the ICDM '18 paper "SINE: Scalable Incomplete Network Embedding". The
  procedure implicitly factorizes a joint adjacency matrix power and feature matrix.
  The decomposition happens on truncated random walks and the adjacency matrix powers
  are pooled together.
  
  Args:
      walk_number (int): Number of random walks. Default is 10.
      walk_length (int): Length of random walks. Default is 80.
      dimensions (int): Dimensionality of embedding. Default is 128.
      workers (int): Number of cores. Default is 4.
      window_size (int): Matrix power order. Default is 5.
      epochs (int): Number of epochs. Default is 1.
      use_hierarchical_softmax (bool): Whether to use hierarchical softmax or negative sampling to train the model.
      number_of_negative_samples (int): Number of negative nodes to sample (usually between 5-20). If set to 0, no negative sampling is used. Default is 5.
      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,
      walk_number: int = 10,
      walk_length: int = 80,
      dimensions: int = 128,
      workers: int = 4,
      window_size: int = 5,
      epochs: int = 1,
      use_hierarchical_softmax: bool = True,
      number_of_negative_samples: int = 5,
      learning_rate: float = 0.05,
      min_count: int = 1,
      seed: int = 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.use_hierarchical_softmax = use_hierarchical_softmax
      self.number_of_negative_samples = number_of_negative_samples
      self.learning_rate = learning_rate
      self.min_count = min_count
      self.seed = seed
  
file_path: karateclub/karateclub/node_embedding/attributed/sine.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.word2vec import Word2Vec
  from karateclub.utils.walker import RandomWalker
label_desc: Use image node_id 1 to create a new SINE object from inherited base classes: Estimator with example: obj = SINE(walk_number, walk_length, dimensions, workers, window_size, epochs, use_hierarchical_softmax, number_of_negative_samples, learning_rate, min_count, seed)