class_docstr: General Purpose Similarity Graph which is used to perform graph analysis.
  
  The nodes in graph represent general purpose object such as concept, word, phrase,
  sentence, document, entity.
  
  The edges in graph represent semantic similarity between nodes. The similarity score between
  objects is ranged in [0,1] and computed by specific similarity measures.
class_docstr_tok: ["General","Purpose","Similarity","Graph","which","is","used","to","perform","graph","analysis",".","The","nodes","in","graph","represent","general","purpose","object","such","as","concept",",","word",",","phrase",",","sentence",",","document",",","entity",".","The","edges","in","graph","represent","semantic","similarity","between","nodes",".","The","similarity","score","between","objects","is","ranged","in","[","0,1","]","and","computed","by","specific","similarity","measures","."]
code: def similarity_graph(self, M):
      return nx.from_numpy_matrix(M)
  
imports: from abc import ABCMeta, abstractmethod
  from collections import deque
  from itertools import combinations
  import networkx
  import numpy
  from sematch.semantic.ontology import DBpedia
  from sematch.utility import FileIO
  from sematch.semantic.sparql import StatSPARQL
  import math
label_desc: Use image node_id 3 for calling the SimGraph obj's underlying member method code with example usage: obj.similarity_graph(M) and returns: nx