g = standardize(WS2D(NODES, edges, radius, weak_ties))
print '# Edges %d\tAverage Clustering = %f' % (countEdges(g) * 2, ac(
und(g)))
fi(g) # Fill incoming edges
sys.stdout.flush()
# ## Execute centrality measures
# In[5]:
print '# Page Rank execution...'
pagerank, iterations, err = pr(g, alpha=1.0e-5, eps=1.0e-3)
print '#', iterations, ' iterations. Error:', err
top_pr = [a for a, b in topk(pagerank, seed)]
# In[6]:
print '# Eigenvector Centrality...',
cscores, diffsum = ec(g)
top_eigenc = [a for a, b in topk(cscores, seed)]
print '# Done'
# In[7]:
print '# Betweennes centrality...',
bet = betweenness(g)
top_bet = [a for a, b in topk(bet, seed)]
print '# Done'
print '# Cascade Expansion Wiki-Vote.txt' print '# Edges = %d\tAverage Clustering = %f'% (countEdges(g), ac(und(g))) fi(g) # Fill incoming edges dictionary sys.stdout.flush() # ## Execute centrality measures # In[8]: print '# Page Rank execution...' pagerank, iterations, err = pr(g, alpha=1.0e-5, eps=1.0e-3) print '#',iterations, ' iterations. Error:', err top_pr = [a for a,b in topk(pagerank, nodes)] # In[9]: print '# Eigenvector Centrality...', cscores, diffsum = ec(g) top_eigenc = [a for a, b in topk(cscores, nodes)] print '# Done' # In[10]: print '# Betweennes centrality...', bet = betweenness(g) top_bet = [a for a, b in topk(bet, nodes)]
from mylesson5 import eigenvector_centrality as ec
from dgraph import diameter
from dgraph import readGraph
from dgraph import Page_Rank as pr
from dgraph import fill_incoming as fi
import networkx as nx
seed = 100
g = readGraph('wiki-Vote.txt')
# G = nx.from_dict_of_lists(g)
# print 'NetworkX Page Rank'
# print [a for a,b in topk(nx.pagerank(G, alpha=1.0e-6, tol=1.0e-10), 10)]
# print [a for a,b in topk(nx.eigenvector_centrality(G), 10)]
# g = {0: [2, 3, 4], 1: [0, 2, 3], 2: [1], 3: [0, 4], 4: [0]}
fi(g)
print 'Incoming edges stored'
# print 'Nodes: ', len(g.keys())
# print 'Diameter: ', diameter(g)
print 'Page Rank execution...'
# print 'Triangles: ', ctD(g)
pagerank, iterations, err = pr(g, alpha=1.0e-5, eps=1.0e-8) # alpha = 0.00001
print iterations, ' iterations. Error:', err
print 'Page Rank'
print topk(pagerank, seed)
# print 'Eigenvector Centrality'
# cscores, diffsum = ec(g)
# print [a for a, b in topk(cscores, 10)]
# bet = betweenness(g)
# print 'Betweennes centrality'
# print [a for a, b in topk(bet, 10)]
# In[6]:
g = GenWSGridGraph(NODES, edges, radius, weak_ties)
print '# Edges %d\tAverage Clustering = %f' % (countEdges(g)*2,ac(und(g)))
fi(g) # Fill incoming edges dictionary
# ## Execute centrality measures
# In[12]:
print '# Page Rank execution...'
pagerank, iterations, err = pr(g, alpha=1.0e-5, eps=1.0e-3)
print '#',iterations, ' iterations. Error:', err
top_pr = [a for a,b in topk(pagerank, seed)]
# In[13]:
print '# Eigenvector Centrality...',
ecscores, _ = ec(g)
top_eigenc = [a for a, b in topk(ecscores, seed)]
print '# Done'
# In[14]:
print '# Betweennes centrality...',
bet = betweenness(g)
top_bet = [a for a, b in topk(bet, seed)]
# In[6]: g = GenWSGridGraph(NODES, edges, radius, weak_ties) print '# Edges %d\tAverage Clustering = %f' % (countEdges(g)*2,ac(und(g))) fi(g) # Fill incoming edges dictionary # ## Execute centrality measures # In[12]: print '# Page Rank execution...' pagerank, iterations, err = pr(g, alpha=1.0e-5, eps=1.0e-3) print '#',iterations, ' iterations. Error:', err top_pr = [a for a,b in topk(pagerank, NODES)] # In[13]: print '# Eigenvector Centrality...', ecscores, _ = ec(g) top_eigenc = [a for a, b in topk(ecscores, NODES)] print '# Done' # In[14]: print '# Betweennes centrality...', bet = betweenness(g) top_bet = [a for a, b in topk(bet, NODES)]
#!/usr/bin/python
import sys
from lesson3 import readGraph, count2Paths, diameter
from lesson4 import betweenness, cb_max, topk
simple_graph = {}
simple_graph[0] = {1}
simple_graph[1] = {0, 2}
simple_graph[2] = {1, 3}
simple_graph[3] = {2, 4}
simple_graph[4] = {3}
cb = betweenness(simple_graph, True)
# graph = readGraph(sys.argv[1])
# cb = betweenness(graph)
k = 10
print 'Top ', k, '=>', topk(cb, k)
# In[4]: g = rdbg(NODES, p, edges) print '# Edges %d\tAverage Clustering = %f' % (countEdges(g) * 2, ac(und(g))) fi(g) # Fill incoming edges sys.stdout.flush() # ## Execute centrality measures # In[6]: print '# Page Rank execution...' pagerank, iterations, err = pr(g, alpha=1.0e-5, eps=1.0e-3) print '#', iterations, ' iterations. Error:', err top_pr = [a for a, b in topk(pagerank, NODES)] # In[7]: print '# Eigenvector Centrality...', cscores, diffsum = ec(g) top_eigenc = [a for a, b in topk(cscores, NODES)] print '# Done' # In[8]: print '# Betweennes centrality...', bet = betweenness(g) top_bet = [a for a, b in topk(bet, NODES)] print '# Done'
#!/usr/bin/python
import sys
from lesson4 import topk, lastk
from dgraph import readGraph
from dgraph import Page_Rank as pr
from dgraph import Independent_Cascade as ic
seed = int(sys.argv[1])
g = readGraph('wiki-Vote.txt')
pagerank, iterations, err = pr(g, alpha=1.0e-5, eps=1.0e-6) # alpha = 0.00001
# print pagerank
print 'Page Rank. %s iterations. %s accuracy' % (iterations, err)
top = [a for a,b in topk(pagerank, seed)]
last = [a for a,b in lastk(pagerank, seed)]
print 'Top', seed
print [(u, pagerank[u]) for u in top]
print 'Last', seed
print [(u, pagerank[u]) for u in last]
adopters, haters, steps = ic(g, top)
print 'Independent Cascade Model: TOP', seed
print 'Final Adopters:', len(adopters)
print 'Final Haters:', len(haters)
print '# Iterations:', steps
adopters, haters, steps = ic(g, last)
print 'Independent Cascade Model: LAST', seed
print 'Final Adopters:', len(adopters)
print 'Final Haters:', len(haters)
print '# Iterations:', steps
#!/usr/bin/python
import sys
from lesson4 import topk, lastk
from dgraph import readGraph
from dgraph import Page_Rank as pr
from dgraph import Independent_Cascade as ic
seed = int(sys.argv[1])
g = readGraph('wiki-Vote.txt')
pagerank, iterations, err = pr(g, alpha=1.0e-5, eps=1.0e-6) # alpha = 0.00001
# print pagerank
print 'Page Rank. %s iterations. %s accuracy' % (iterations, err)
top = [a for a, b in topk(pagerank, seed)]
last = [a for a, b in lastk(pagerank, seed)]
print 'Top', seed
print[(u, pagerank[u]) for u in top]
print 'Last', seed
print[(u, pagerank[u]) for u in last]
adopters, haters, steps = ic(g, top)
print 'Independent Cascade Model: TOP', seed
print 'Final Adopters:', len(adopters)
print 'Final Haters:', len(haters)
print '# Iterations:', steps
adopters, haters, steps = ic(g, last)
print 'Independent Cascade Model: LAST', seed
print 'Final Adopters:', len(adopters)
print 'Final Haters:', len(haters)
print '# Iterations:', steps
nodes = len(g.keys()) print '# Cascade Expansion Wiki-Vote.txt' print '# Edges = %d\tAverage Clustering = %f' % (countEdges(g), ac(und(g))) fi(g) # Fill incoming edges dictionary sys.stdout.flush() # ## Execute centrality measures # In[8]: print '# Page Rank execution...' pagerank, iterations, err = pr(g, alpha=1.0e-5, eps=1.0e-3) print '#', iterations, ' iterations. Error:', err top_pr = [a for a, b in topk(pagerank, nodes)] # In[9]: print '# Eigenvector Centrality...', cscores, diffsum = ec(g) top_eigenc = [a for a, b in topk(cscores, nodes)] print '# Done' # In[10]: print '# Betweennes centrality...', bet = betweenness(g) top_bet = [a for a, b in topk(bet, nodes)] print '# Done'