class_docstr: An implementation of `"NNSED"
  <http://www.bigdatalab.ac.cn/~shenhuawei/publications/2017/cikm-sun.pdf>`_
  from the CIKM '17 paper "A Non-negative Symmetric Encoder-Decoder Approach
  for Community Detection". The procedure uses non-negative matrix factorization
  in order to learn an unnormalized cluster membership distribution over nodes.
  The method can be used in an overlapping and non-overlapping way.
  
  Args:
      layers (int): Embedding layer size. Default is 32.
      iterations (int): Number of training epochs. Default 10.
      seed (int): Random seed for weight initializations. Default 42.
      noise (float): Random noise for normalization stability. Default is 10**-6.
class_docstr_tok: ["An","implementation","of","`","``","NNSED","''","<","http",":","\/\/www.bigdatalab.ac.cn\/~shenhuawei\/publications\/2017\/cikm-sun.pdf",">","`","_","from","the","CIKM","'17","paper","``","A","Non-negative","Symmetric","Encoder-Decoder","Approach","for","Community","Detection","''",".","The","procedure","uses","non-negative","matrix","factorization","in","order","to","learn","an","unnormalized","cluster","membership","distribution","over","nodes",".","The","method","can","be","used","in","an","overlapping","and","non-overlapping","way",".","Args",":","layers","(","int",")",":","Embedding","layer","size",".","Default","is","32.","iterations","(","int",")",":","Number","of","training","epochs",".","Default","10.","seed","(","int",")",":","Random","seed","for","weight","initializations",".","Default","42.","noise","(","float",")",":","Random","noise","for","normalization","stability",".","Default","is","10","*","*","-6","."]
code: def __init__(
      self,
      dimensions: int = 32,
      iterations: int = 10,
      seed: int = 42,
      noise: float = 10**-6,
  ):
      self.dimensions = dimensions
      self.iterations = iterations
      self.seed = seed
      self.noise = noise
  
file_path: karateclub/karateclub/community_detection/overlapping/nnsed.py
imports: import numpy
  import networkx
  from typing import Dict
  from scipy import sparse
  from karateclub.estimator import Estimator
label_desc: Use image node_id 1 to create a new NNSED object from inherited base classes: Estimator with example: obj = NNSED(dimensions, iterations, seed, noise)