Create PageRank

PageRank

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+import networkx as nx 
+import numpy as np 
+import pandas as pd 
+import matplotlib.pyplot as plt 
+import operator 
+import random as rd
+graph=nx.gnp_random_graph(25,0.6,directed= True)
+nx.draw(graph,with_labels=True,font_color='red',font_size=10,node_color='yellow')
+plt.show()
+count=graph.number_of_nodes()
+print(list(graph.neighbors(1)))
+
+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")
+for j in range(0,25):
+    rank_dict[j]=rank_dict[j]/600000
+pagerank = nx.pagerank(graph)
+
+pagerank_sorted = sorted(pagerank.items(),key = lambda v: (v[1], v[0]), reverse = True)
+pagerank_sorted
+
+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=" ")