class_docstr: An implementation of `"BigClam" <http://infolab.stanford.edu/~crucis/pubs/paper-nmfagm.pdf>`_
  from the WSDM '13 paper "Overlapping Community Detection at Scale: A Non-negative Matrix
  Factorization Approach". The procedure uses gradient ascent to create an embedding which is
  used for deciding the node-cluster affiliations.
  
  Args:
      dimensions (int): Number of embedding dimensions. Default 8.
      iterations (int): Number of training iterations. Default 50.
      learning_rate (float): Gradient ascent learning rate. Default is 0.005.
      seed (int): Random seed value. Default is 42.
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code: def __init__(
      self,
      dimensions: int = 8,
      iterations: int = 50,
      learning_rate: float = 0.005,
      seed: int = 42,
  ):
      self.dimensions = dimensions
      self.iterations = iterations
      self.learning_rate = learning_rate
      self.seed = seed
  
file_path: karateclub/karateclub/community_detection/overlapping/bigclam.py
imports: import random
  import community
  import numpy
  import networkx
  from typing import Dict
  from karateclub.estimator import Estimator
label_desc: Use image node_id 1 to create a new BigClam object from inherited base classes: Estimator with example: obj = BigClam(dimensions, iterations, learning_rate, seed)