def UPAalgo(n,m):
m_graph = make_complete_graph(m)
random_connect = UPA(m)
for i in xrange(m,n):
if i not in m_graph.keys():
m_graph[i] = set()
m_graph[i] = random_connect.run_trial(m)
for item in m_graph[i]:
m_graph[item].update([i])
#m_graph[i] = random_connect.run_trial(m)
return m_graph
def make_random_graph_UPA(m,n):
random_graph = make_complete_graph(m)
for dummy in range(n-m):
random_graph[dummy + m] = set()
initial_graph = UPATrial(m)
for dummy in range(n-m):
new_node_neighbors = initial_graph.run_trial(m)
random_graph[dummy + m] = new_node_neighbors
for new_neighbor in new_node_neighbors:
random_graph[new_neighbor].add(dummy + m)
return random_graph
def upa_makegraph(num_nodes, iter_nodes):
"""
Generates graph by UPA algorithm where num_nodes is a total number of nodes
and iter_nodes is a number of edges added on each iteration
"""
result = prj1.make_complete_graph(iter_nodes)
upa = UPATrial(iter_nodes)
if num_nodes <= iter_nodes:
return result
for step in range(iter_nodes, num_nodes):
nodes_to_be_added = upa.run_trial(iter_nodes)
result[step] = nodes_to_be_added
for node in nodes_to_be_added:
result[node].add(step)
return result
def upa_makegraph(num_nodes, iter_nodes):
"""
Generates graph by UPA algorithm where num_nodes is a total number of nodes
and iter_nodes is a number of edges added on each iteration
"""
result = prj1.make_complete_graph(iter_nodes)
upa = UPATrial(iter_nodes)
if num_nodes <= iter_nodes:
return result
for step in range(iter_nodes, num_nodes):
nodes_to_be_added = upa.run_trial(iter_nodes)
result[step] = nodes_to_be_added
for node in nodes_to_be_added:
result[node].add(step)
return result
def upa_undigraph_gen(node_num, step_node):
'''
generate un-digraph use UPA Algorithm
Parameters
----------
node_num : int
step_node : int
Returns
-------
graph : dict
'''
UPAGraph = UPATrial(step_node)
graph = make_complete_graph(step_node)
for i in range(step_node, node_num):
neighbors = UPAGraph.run_trial(step_node)
graph[i] = neighbors
for nei in neighbors:
graph[nei] = graph.get(nei, set()).union({i})
return graph