class_docstr: An implementation of `"GL2Vec" <https://link.springer.com/chapter/10.1007/978-3-030-36718-3_1>`_
  from the ICONIP '19 paper "GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features".
  First, the algorithm creates the line graph of each graph in the graph dataset.
  The procedure creates Weisfeiler-Lehman tree features for nodes in graphs. Using
  these features a document (graph) - feature co-occurrence matrix is decomposed in order
  to generate representations for the graphs.
  
  Args:
      wl_iterations (int): Number of Weisfeiler-Lehman iterations. Default is 2.
      dimensions (int): Dimensionality of embedding. Default is 128.
      workers (int): Number of cores. Default is 4.
      down_sampling (float): Down sampling frequency. Default is 0.0001.
      epochs (int): Number of epochs. Default is 10.
      learning_rate (float): HogWild! learning rate. Default is 0.025.
      min_count (int): Minimal count of graph feature occurrences. Default is 5.
      seed (int): Random seed for the model. Default is 42.
      erase_base_features: (bool): Whether to delete the base features.
code: def __init__(
      self,
      wl_iterations: int = 2,
      dimensions: int = 128,
      workers: int = 4,
      down_sampling: float = 0.0001,
      epochs: int = 10,
      learning_rate: float = 0.025,
      min_count: int = 5,
      seed: int = 42,
      erase_base_features: bool = False,
  ):
      self.wl_iterations = wl_iterations
      self.dimensions = dimensions
      self.workers = workers
      self.down_sampling = down_sampling
      self.epochs = epochs
      self.learning_rate = learning_rate
      self.min_count = min_count
      self.seed = seed
      self.erase_base_features = erase_base_features
  
file_path: karateclub/karateclub/graph_embedding/gl2vec.py
imports: from typing import Optional
  import numpy
  import networkx
  from typing import List
  from karateclub.estimator import Estimator
  from gensim.models.doc2vec import Doc2Vec, TaggedDocument
  from karateclub.utils.treefeatures import WeisfeilerLehmanHashing
label_desc: Use image node_id 1 to create a new GL2Vec object from inherited base classes: Estimator with example: obj = GL2Vec(wl_iterations, dimensions, workers, down_sampling, epochs, learning_rate, min_count, seed, erase_base_features)