Apply PageRank link authority algorithm to a list of Twitter users, as a first-pass measure for user authority

pagerank_twitter_users.py

#pagerank calculation for a list of Twitter users
#implementing this was a bit of a head-scratcher...

f1="C:\\somedir\\list_of_edges_between_usrs.txt"  #our social graph, as an edge-list
file1=open(f1,'r') 
edges=file1.readlines()

l_nodes,l_outd,nodes2=[],[],[]
l_score, l_score_new, l_out_score=[],[],[]

#find all unique nodes
print 'getting unique nodes and outdegree of each...'
for x in range(len(edges)):
    tab=edges[x].find('\t')
    node=edges[x][:tab]
    node2=edges[x][tab+1:-1]
    nodes2.append(node2)
    if node in l_nodes:
        index=l_nodes.index(node)
        l_outd[index]+=1
    else:
        l_nodes.append(node)
        l_outd.append(1)

nodes3=[]

for node2 in nodes2:
    if node2 in nodes3:
        pass
    else:
        nodes3.append(node2)

for x in range(len(nodes3)):
    if nodes3[x] not in l_nodes:
        l_nodes.append(nodes3[x])
        l_outd.append(1)

print 'unique nodes, outdegree done'

#create a parallel list of pagerank score. instantiate each score to 1
print 'instantiating pagerank scores @ 1...'
for x in range(len(l_nodes)):
    l_score.append(1.0)

for x in range(len(l_nodes)):
    l_score_new.append(1.0)
    
print 'getting out-scores...'
for x in range(len(l_score)):
    l_out_score.append(l_score[x]/l_outd[x])

#get new pagerank scores
#iterate 49 times ... scores converge somewhere between 30-50 iterations (save every iteration to file to make sure they HAVE converged)
    
for n in range(49):
    print 'Interation '+str(n)
    fw="C:\\pagerank\\iter_"+str(n+1)+".txt"
    filew=open(fw,'w')
    #swap new list into old list, reset new list with 0s
    for a in range(len(l_score)):
        l_score[a]=l_score_new[a]
                
    for b in range(len(l_score_new)):
        l_score_new[b]=0.0
        
    #get new outbound scores
    for c in range(len(l_out_score)):
        l_out_score[c]=l_score[c]/l_outd[c]
        
    #now get new scores based on new outbound scores
    for x in range(len(edges)):
        tab=edges[x].find('\t')
        fllr=edges[x][:tab]
        flld=edges[x][tab+1:-1]
        fllr_index=l_nodes.index(fllr)
        fllr_score=l_out_score[fllr_index]
        flld_index=l_nodes.index(flld)
        l_score_new[flld_index]+=fllr_score
        
    #apply damping factor of d=.85
d=0.85
    for m in range(len(l_score_new)):
        l_score_new[m]=(1.0-d)+d*l_score_new[m]
        
    for x in range(len(l_nodes)):
        filew.write(str(l_nodes[x])+","+str(l_outd[x])+","+str(l_out_score[x])+','+str(l_score[x])+','+str(l_score_new[x])+'\n')

    filew.close()