class_docstr: An implementation of `"NodeSketch" <https://exascale.info/assets/pdf/yang2019nodesketch.pdf>`_
  from the KDD '19 paper "NodeSketch: Highly-Efficient Graph Embeddings
  via Recursive Sketching". The procedure  starts by sketching the self-loop-augmented
  adjacency matrix of the graph to output low-order node embeddings, and then recursively
  generates k-order node embeddings based on the self-loop-augmented adjacency matrix
  and (k-1)-order node embeddings.
  
  Args:
      dimensions (int): Embedding dimensions. Default is 32.
      iterations (int): Number of iterations (sketch order minus one). Default is 2.
      decay (float): Exponential decay rate. Default is 0.01.
      seed (int): Random seed value. Default is 42.
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code: def __init__(
      self,
      dimensions: int = 32,
      iterations: int = 2,
      decay: float = 0.01,
      seed: int = 42,
  ):
      self.dimensions = dimensions
      self.iterations = iterations
      self.decay = decay
      self.seed = seed
      self._weight = self.decay / self.dimensions
  
file_path: karateclub/karateclub/node_embedding/neighbourhood/nodesketch.py
imports: import networkx
  import numpy
  from collections import Counter
  from karateclub.estimator import Estimator
label_desc: Use image node_id 1 to create a new NodeSketch object from inherited base classes: Estimator with example: obj = NodeSketch(dimensions, iterations, decay, seed)