class_docstr: An implementation of `"Edge Motif Clustering" <https://arxiv.org/abs/1906.04560>`_
  from the KDD '19 paper "EdMot: An Edge Enhancement Approach for Motif-aware Community Detection". The tool first creates
  the graph of higher order motifs. This graph is clustered by the Louvain method. The resulting
  cluster memberships are stored as a dictionary.
  
  Args:
      component_count (int): Number of extracted motif hypergraph components. Default is 2.
      cutoff (int): Motif edge cut-off value. Default is 50.
      seed (int): Random seed value. Default is 42.
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code: def __init__(
      self, component_count: int = 2, cutoff: int = 50, seed: int = 42
  ):
      self.component_count = component_count
      self.cutoff = cutoff
      self.seed = seed
  
file_path: karateclub/karateclub/community_detection/non_overlapping/edmot.py
imports: import community
  import networkx
  from typing import Dict
  from karateclub.estimator import Estimator
label_desc: Use image node_id 1 to create a new EdMot object from inherited base classes: Estimator with example: obj = EdMot(component_count, cutoff, seed)