class_docstr: An implementation of `"SocioDim" <https://dl.acm.org/doi/abs/10.1145/1557019.1557109>`_
  from the KDD '09 paper "Relational Learning via Latent Social Dimensions".
  The procedure extracts the eigenvectors corresponding to the largest eigenvalues
  of the graph modularity matrix. These vectors are used as the node embedding.
  
  Args:
      dimensions (int): Dimensionality of embedding. Default is 128.
      seed (int): Random seed value. Default is 42.
docstr: Fitting a Social Dimensions model.
  
  Arg types:
      * **graph** *(NetworkX graph)* - The graph to be embedded.
code: def fit(self, graph: nx.classes.graph.Graph):
      """
      Fitting a Social Dimensions model.
  
      Arg types:
          * **graph** *(NetworkX graph)* - The graph to be embedded.
      """
      self._set_seed()
      graph = self._check_graph(graph)
      number_of_nodes = graph.number_of_nodes()
      L_tilde = nx.modularity_matrix(
          graph, nodelist=range(number_of_nodes)
      )
      _, self._embedding = sps.linalg.eigsh(
          L_tilde,
          k=self.dimensions,
          which="LM",
          return_eigenvectors=True,
      )
  
file_path: karateclub/karateclub/node_embedding/neighbourhood/sociodim.py
imports: import numpy
  import networkx
  import scipy.sparse
  from karateclub.estimator import Estimator
label_desc: Use image node_id 2 for calling the SocioDim obj's underlying member method code with example usage: obj.fit(graph) without return types