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: Calculating the neighbourhood overlap for a pair of nodes.
  
  Arg types:
      * **node_1** *(int)* - Source node 1.
      * **node_2** *(int)* - Source node 2.
  Return types:
      * **overlap** *(int)* - Neighbourhood overlap score.
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code: def _overlap(self, node_1, node_2):
      """
      Calculating the neighbourhood overlap for a pair of nodes.
  
      Arg types:
          * **node_1** *(int)* - Source node 1.
          * **node_2** *(int)* - Source node 2.
      Return types:
          * **overlap** *(int)* - Neighbourhood overlap score.
      """
      nodes_1 = self._graph.neighbors(node_1)
      nodes_2 = self._graph.neighbors(node_2)
      overlap = len(set(nodes_1).intersection(set(nodes_2)))
      return overlap
  
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 2 for calling the EdMot obj's underlying member method code with example usage: obj._overlap(node_1, node_2) and returns: overlap