Implementation of pagerank algorithm using python networkx library

Page Rank Algorithm.py

# -*- coding: utf-8 -*-
"""Page Rank Algorithm.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1oUC_418I6e2nv_2xBQ0sgXZtDfA98zuH
"""

cd /content/drive/My Drive/medium blogs/Page Rank Algorithm

import networkx as nx
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import operator
import random as rd

# created a directed graph
graph=nx.gnp_random_graph(25,0.6,directed=True)

#draw a graph
nx.draw(graph,with_labels=True,font_color='red',font_size=10,node_color='yellow')

#plot a graph

plt.show()

#number of nodes for graph
count=graph.number_of_nodes()
#graph neighbours of a node 1
print(list(graph.neighbors(1)))

#Page Rank Algorithm-Calculating random walk score
rank_dict={}
x=rd.randint(0,25)
for j in range(0,25):
  rank_dict[j]=0
rank_dict[x]=rank_dict[x]+1
for i in range(600000):
  list_n=list(graph.neighbors(x))
  if(len(list_n)==0):
    x=rd.randint(0,25)
    rank_dict[x]=rank_dict[x]+1
  else:
    x=rd.choice(list_n)
    rank_dict[x]=rank_dict[x]+1
  
print("Random Walk Score Updated")

#normalising values
for j in range(0,25):
  rank_dict[j]=rank_dict[j]/600000

#Page rank by networkx library
pagerank=nx.pagerank(graph)

#sorting both dictionaries based on items
pagerank_sorted=sorted(pagerank.items(),key=lambda v:(v[1],v[0]),reverse=True)

pagerank_sorted

#sorting the rank_dict based on values
rank_dict_sorted=sorted(rank_dict.items(),key=lambda v:(v[1],v[0]),reverse=True)

rank_dict_sorted

print("The order generated by our implementation algorithm is\n")
for i in rank_dict_sorted:
  print(i[0],end=" ")
print("\n\nThe order generated by networkx library is\n")
for i in pagerank_sorted:
  print(i[0],end=" ")