class_docstr: An implementation of `"SCD" <http://wwwconference.org/proceedings/www2014/proceedings/p225.pdf>`_ from the
  WWW '14 paper "High Quality, Scalable and Parallel Community Detection for
  Large Real Graphs". The procedure greedily optimizes the approximate weighted
  community clustering metric. First, clusters are built around highly clustered nodes.
  Second, we refine the initial partition by using the approximate WCC. These refinements
  happen for the whole vertex set.
  
  Args:
      iterations (int): Refinemeent iterations. Default is 25.
      eps (float): Epsilon score for zero division correction. Default is 10**-6.
      seed (int): Random seed value. Default is 42.
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docstr: Initial local clustering coefficient based cluster membership assignments.
docstr_tok: ["Initial","local","clustering","coefficient","based","cluster","membership","assignments","."]
code: def _create_initial_partition(self):
      """
      Initial local clustering coefficient based cluster membership assignments.
      """
      self._clustering_coefficient = nx.clustering(self._graph)
      self._cc_pairs = [
          (node_cc**0.5, node)
          for node, node_cc in self._clustering_coefficient.items()
      ]
      self._cc_pairs = sorted(self._cc_pairs, key=lambda tup: tup[0])[
          ::-1
      ]
      self._do_initial_assignments()
  
file_path: karateclub/karateclub/community_detection/non_overlapping/scd.py
imports: import networkx
  from typing import Dict
  from karateclub.estimator import Estimator
label_desc: Use image node_id 4 for calling the SCD obj's underlying member method code with example usage: obj._create_initial_partition() without return types