def generator(n):
'''Generator for our social graph.
Our graph will have n nodes, it will be a Dorogovtsev-Mendes's Graph, and
it will handle reputation (for nodes) and affect (for edges).'''
G = nx.dorogovtsev_goltsev_mendes_graph(1)
'''In this loop we generate a raw network'''
for i in range(n-3):
r = random.choice(range(nx.number_of_edges(G)))
e = nx.edges(G)
e = e[r] #e is the selected edge
nn = G.number_of_nodes() #nn is the new node number
G.add_node(nn)
G.add_edge(nn, e[0])
G.add_edge(nn, e[1])
'''In this bloc we set reputations'''
reput = {}
md = graphlib.max_degree(G)
for i in range(G.number_of_nodes()):
reput[i] = random.random() #float(G.degree(i))/float(md)
nx.set_node_attributes(G,'reput',reput)
'''And then we set affect'''
affect = {}
e = nx.edges(G)
for i in range(G.number_of_edges()):
affect[e[i]] = random.random()
nx.set_edge_attributes(G,'affect',affect)
return G
e = nx.edges(G3)
e = e[r] #e is the selected edge
nn = G3.number_of_nodes() #nn is the new node number
G3.add_node(nn)
G3.add_edge(nn, e[0])
G3.add_edge(nn, e[1])
listG = [G1,G2,G3]
for G in listG:
'''In this bloc we set reputations'''
reput = {}
md = graphlib.max_degree(G)
for i in range(G.number_of_nodes()):
reput[i] = random.random() #float(G.degree(i))/float(md)
nx.set_node_attributes(G,'reput',reput)
'''And then we set affect'''
affect = {}
e = nx.edges(G)
for i in range(G.number_of_edges()):
affect[e[i]] = random.random()
nx.set_edge_attributes(G,'affect',affect)
layout = nx.spring_layout(G3)
#graphlib.plot_graphs([G1,G2,G3], layout)