from degree_distribution_for_graphs import normalized_in_degree_distribution
def er(n, p):
"""
Implmentation of Erdos-Renyi algorithm
to generate random directed graph.
Returns:
A directed graph.
"""
digraph = {key: set() for key in xrange(n)}
for u in xrange(n):
for v in xrange(n):
if u != v:
if random.random() < p:
digraph[u].add(v)
return digraph
digraph = er(1000, 0.2)
distribution = normalized_in_degree_distribution(digraph)
plt.title("Normalized in-degree distribution (Point graph)")
plt.xlabel("In-degrees (log)")
plt.ylabel("Normalized Values (log)")
plt.loglog(distribution.keys(), distribution.values(), "ro")
plt.show()
# compute the neighbors for the newly-created node
new_node_neighbors = set()
for dummy_idx in range(num_nodes):
new_node_neighbors.add(random.choice(self._node_numbers))
# update the list of node numbers so that each node number
# appears in the correct ratio
self._node_numbers.append(self._num_nodes)
self._node_numbers.extend(list(new_node_neighbors))
#update the number of nodes
self._num_nodes += 1
return new_node_neighbors
def DPA(nodes, m):
dpa_graph = make_complete_graph(m)
dpa = DPATrial(m)
for i in range(nodes-m):
v_dash = dpa.run_trial(m)
dpa_graph[i+m] = v_dash
return dpa_graph
normalized_digraph = normalized_in_degree_distribution(DPA(27770, 12))
plt.title("DPA Algorithm")
plt.xlabel("DPA Graph In-degrees (log)")
plt.ylabel("Probability (log)")
plt.loglog(normalized_digraph.keys(), normalized_digraph.values(), 'ro')
plt.show()
