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This is a Python code for the PageRank algorithm with some values inserted at the bottom. ```python import numpy as np N = 301 # The number of pages in your corpus. d = 0.85 # The probability of navigating from a random page to a random link # Page 301 links: # [pageid1,pageid2,...] -> Links outward. pages_301_links = [ [101,105,111,112], [], [], [108,122,136,140,201], [128,141,146], [], [], [], [], [], [], [102,121,127,143], [104,116,129,130], [114,131], [109,123,137,139,142], [103,110,118,132,136,140,201,217], [], [], [107,125,133], [], [113,126,144], [], [111,130,134], [], [105,106,112,123,133,137,142,202], [116,131,132,143,149,154], [], [114,119,121,127,129,138,150,206], [], [108,118,128,135,144,146,160], [125,139,201,215], [], [107,109,122,136,145,152,162], [], [], [], [], [], [119,140,149,156], [], [124,132,150,159], [], [], [105,128,139,141,148], [], [110,126,135,146,155], [137,161,168], [], [112,113,133,147,152,158,166], [], [], [125,134,140,153,164], [], [118,120,123,129,144,162,170,176], [], [], [130,132,137,139,142,147,161,166,172,180], [115,121,145,154,167,179,184,191,200], [], [], [136,148,152,164,170,182,188,196,203], [], [], [143,157,168,171,175,184,197,204,210], [], [], [149,165,177,186,193,201,212], [], [150,158,174,187,194,205,215], [146,159,166,173,180,190,209,221], [], [160,176,189,196,208,217,225], [163,184,202,214,222], [], [], [], [167,191,203,219,227], [], [172,197,211,224], [170,198,218], [], [178,206,223,232,240], [175,205,220], [181,215,230,238,245], [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 ], [180,198,225,236], [], [ 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53 ], [32,40], [], [], [], [], [ 33,41], [] ] ] Nlinks_301 = len(pages_301_links[0]) Nlinks = [] for i in range(0,N+1): Nlinks.append(len(pages_301_links[i])) # Calculate number of external links. external_links = [] n_externals = [] i=0 while i < N: Nlinks[i] = int(Nlinks[i]) i+=1 i=0 while i < (N+1): external_links.append(0) n_externals.append(0) i += 1 i = 301 external_linker= False extcount = 0 while i >= 1: ext= pages_301_links[i-1][0] while len(pages_301_links[i -1])> ext: link_page=pages_301_links[i -1][ext] if i==link_page: continue nlinks_link_page=Nlinks[link_page] - external_links[link_page-1]+Nexternal(link_page) link_to_other_pages=int(0) if Nlinks_link_page != 0: link_to_other_pages = (Nlinks[i -1] - external_links[i-1]) external_linker= True link_to_thispage=link_to_other_pages/(Nlinks[link_page]-external_links[link_page]) n_externals[link_page] += 1 # update NExternal() ext+=1 else: continue external_linker=False if(external_links[i-1]>0): # we need to add external_link(i) back new_NExternal=i Nexternal=external_links[i-1] N_external+=new_Nexternal+ n_externals[i] extcount +=Nexternal i-= 1 # initialize P page-rank for each node in the network as (N). PR = np.zeros((N + 1, 1)) i = 301 PR[300] = d / N while i >0: nlinkers=[] if pages_301_links[i] is None: continue external_link=0 i1=len(pages_301_links[i]) link_page_id = pages_301_links[i][ext] PRlinker =PR[link_page_id]*Nlinks[i]/n_externals[i] PR[i]+=d *PRlinker/Nlinks[link_page_id] i+= 1 i=0 print("\n\nPage Rank :") while i<N+1: print(' Page Number: ',i, ' P_R =\n', end='', sep='') for val in range(9): # 99 decimals of a page. pr = round(PR[i],3) # rounding the decimal values to display 9 PR[i]= float('%.5f' %pr) print("{:.0f} \n".format(pr)) # while len(pages_301_links[i])>ext: i +=1 #PR[N] = d / N