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.
code: def __init__(self, dimensions: int = 128, seed: int = 42):
      self.dimensions = dimensions
      self.seed = seed
  
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 1 to create a new SocioDim object from inherited base classes: Estimator with example: obj = SocioDim(dimensions, seed)