for i in xrange(100):
NODES = 7115
edges = random.randint(75000, 125000)
radius = 2
weak_ties = [i * 5 for i in xrange(0, 4)]
seed = 100
# ##Create a Watts-Strogatz 2D direct graph
# In[4]:
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...',
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)]
# ## Set Parameters
# In[3]:
seed = 100
# ##Read the wiki-vote graph
# In[4]:
g = readGraph('wiki-Vote.txt')
print '# Wiki-Vote.txt'
print '# Edges = %d\tAverage Clustering = %f' % (countEdges(g) * 2, 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, seed)]
# In[9]:
print '# Eigenvector Centrality...',
# ## Set Parameters
# In[3]:
# ##Read the wiki-vote graph
# In[4]:
g = readGraph('wiki-Vote.txt')
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]:
# In[3]:
print '# 100 Random Direct Graphs'
for i in xrange(100):
NODES = 7115
edges = random.randint(75000, 125000)
p = 0.5 # probability
seed = 100
# ##Create a Random Direct Graph
# 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, seed)]
# In[7]: