:param number_total_nodes: total n nodes
:param number_init_nodes: a initial graph with m nodes
:return: a dictionary of a dpa graph
"""
graph_pool = dpat(number_init_nodes)
output_graph = pj1.make_complete_graph(number_init_nodes)
for new_node in range(number_init_nodes, number_total_nodes):
neighbor_nodes = graph_pool.run_trial(number_init_nodes)
output_graph[new_node] = neighbor_nodes
return output_graph
dpa_graph = get_dpa_graph(27770, 13)
id_distribution_dpa = pj1.in_degree_distribution(dpa_graph)
nor_id_distribution_dpa = pj1.normalization(id_distribution_dpa)
#print nor_citation_distribution
fig, ax = plt.subplots()
input_data = [[key, nor_id_distribution_dpa[key]] for key in nor_id_distribution_dpa.keys()]
input_data.sort()
input_x = [component[0] for component in input_data]
input_y = [component[1] for component in input_data]
ax.plot(input_x, input_y)
ax.set_title('normalized distribution of DPA')
ax.set_xscale('log')
ax.set_yscale('log')
plt.show()
:return: dictionary undirected graph which nodes are generated randomly
"""
graph_idd = {}
for node_i in xrange(node_number):
value = []
for node_j in xrange(node_number):
if node_i != node_j:
random_prob = random.random()
if random_prob < prob:
value.append(node_j)
graph_idd[node_i] = value
return graph_idd
er_distribution = pj1.in_degree_distribution(er_algorithm(1000, 0.5))
nor_er_distribution = pj1.normalization(er_distribution)
fig, ax = plt.subplots()
input_data = [[key, nor_er_distribution[key]] for key in nor_er_distribution.keys()]
input_data.sort()
input_x = [component[0] for component in input_data]
input_y = [component[1] for component in input_data]
ax.plot(input_x, input_y)
ax.set_title('Distribution of ER algorithm (1000 nodes, prob is 0.5)')
#ax.set_xscale('log')
#ax.set_yscale('log')
plt.show()