if m == 0:
return graph
#For each node u, we add a node to k randomly chosen nodes
weak_ties = random.choice(k)
while weak_ties > 0:
xt = random.randint(0, line-1)
yt = random.randint(0, line-1)
if xt*line+yt > n-1:
continue
if xt != i and yt != j and xt*line+yt not in graph[i*line+j] and random.random() <= (1/(euclidean_distance((xt, yt), (i,j))**q)):
graph[i*line+j].add(xt*line+yt)
m -= 1
weak_ties -= 1
if m == 0:
return graph
return graph
if __name__ == '__main__':
EXECUTIONS = 3
NODES = 7056
# edges = random.randint(75000, 125000)
edges = [75000, 100000, 125000]
radius = 2
weak_ties = [i*5 for i in xrange(0, 3)]
seed = 100
for i in xrange(EXECUTIONS):
g = GenWSGridGraph(NODES, edges[i], radius, weak_ties)
print 'Edges %d\tAverage Clustering = %f' % (countEdges(g),ac(und(g)))
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]:
#For each node u, we add a node to k randomly chosen nodes
weak_ties = random.choice(k)
while weak_ties > 0:
xt = random.randint(0, line - 1)
yt = random.randint(0, line - 1)
if xt * line + yt > n - 1:
continue
if xt != i and yt != j and xt * line + yt not in graph[
i * line +
j] and random.random() <= (1 / (euclidean_distance(
(xt, yt), (i, j))**q)):
graph[i * line + j].add(xt * line + yt)
m -= 1
weak_ties -= 1
if m == 0:
return graph
return graph
if __name__ == '__main__':
EXECUTIONS = 3
NODES = 7056
# edges = random.randint(75000, 125000)
edges = [75000, 100000, 125000]
radius = 2
weak_ties = [i * 5 for i in xrange(0, 3)]
seed = 100
for i in xrange(EXECUTIONS):
g = GenWSGridGraph(NODES, edges[i], radius, weak_ties)
print 'Edges %d\tAverage Clustering = %f' % (countEdges(g), ac(und(g)))
return len(adopters)
# ## 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]:
# ## 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]: