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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docstr: Fitting an Edge Motif clustering model.
  
  Arg types:
      * **graph** *(NetworkX graph)* - The graph to be clustered.
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code: def fit(self, graph: nx.classes.graph.Graph):
      """
      Fitting an Edge Motif clustering model.
  
      Arg types:
          * **graph** *(NetworkX graph)* - The graph to be clustered.
      """
      self._set_seed()
      graph = self._check_graph(graph)
      self._graph = graph
      self._calculate_motifs()
      self._extract_components()
      self._fill_blocks()
      self._partition = community.best_partition(
          self._graph, random_state=self.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 6 for calling the EdMot obj's underlying member method code with example usage: obj.fit(graph) without return types