def dpa(n, m):
outgraph = graph.make_complete_graph(m)
dpat = DPATrial.DPATrial(m)
i = m
while i < n:
outgraph[i] = dpat.run_trial(m)
i += 1
return outgraph
def dpa(n, m):
outgraph = graph.make_complete_graph(m)
dpat = DPATrial.DPATrial(m)
i = m
while i < n:
outgraph[i] = dpat.run_trial(m)
i += 1
return outgraph
def upa(n,m):
result_graph = graph.make_complete_graph(m)
for i in range(m,n):
edeg = []
for count in range(m):
edeg.append(random.choice(result_graph.keys()))
result_graph[i] = set(edeg)
return result_graph
def dpa_generator(n, m):
"""
This method generates a random directed graph
based on DPA algorithm
Input: n nodes, m connections
"""
new_graph = graph.make_complete_graph(m)
dpa = DPATrial(m)
for idx in range(m, n):
neighbors = dpa.run_trial(m)
new_graph[idx] = neighbors
return new_graph
def dpa_generator(n, m):
"""
This method generates a random directed graph
based on DPA algorithm
Input: n nodes, m connections
"""
new_graph = graph.make_complete_graph(m)
dpa = DPATrial(m)
for idx in range(m, n):
neighbors = dpa.run_trial(m)
new_graph[idx] = neighbors
return new_graph
def upa_generator(n, m):
"""
This method generates a random directed graph
based on UPA algorithm
Input: n nodes, m connections
"""
new_graph = graph.make_complete_graph(m)
#print new_graph
upa = UPATrial(m)
for idx in range(m, n):
neighbors = upa.run_trial(m)
#print neighbors
new_graph[idx] = neighbors
for node in neighbors:
new_graph[node].add(idx)
return new_graph
def upa_generator(n, m):
"""
This method generates a random directed graph
based on UPA algorithm
Input: n nodes, m connections
"""
new_graph = graph.make_complete_graph(m)
#print new_graph
upa = UPATrial(m)
for idx in range(m, n):
neighbors = upa.run_trial(m)
#print neighbors
new_graph[idx] = neighbors
for node in neighbors:
new_graph[node].add(idx)
return new_graph