Reproduction: Reading pseudocode and implementing preferential attachment for scale free networks

Question

I am attempting to reproduce algorithm 5 from the paper: http://vlado.fmf.uni-lj.si/pub/networks/doc/ms/rndgen.pdf

My code is the following:

import random
import numpy as np
from random import randrange
from random import randint
from networkx import empty_graph
def fast_generate_scale_free(n, d):
  #input: number of vertices: n, int
  #minimum degree d, int
  #output: scale-free multigraph 


  G=empty_graph(n)
  M=np.zeros(2 *n *d)
  if d >=1:
    for v in range(0,n):
      for i in range(0, d):
        v= M[int(2*(v*d+i))]
        #draw r uniformly at random
        r=randint(0, 2*(v*d+i))
      
       
        M[int(2*(v*d+i)+1)]=M[r]
        
    for i in range(0, (n*d)):
    
      a=M[int(2*i)]
      b=M[int(2*i+1)]
      G.add_edge(a,b)
     
  return G

Essentially I get an output of (0,0) for any choice of n and d, which I suspect is caused by my initialization of the M array.

I would appreciate any help and this is purely for my own understanding.