def gen_syn2(nb_shapes=100, width_basis=350, feature_generator=None):
basis_type = 'ba'
random_mu = [0.0] * 8
random_sigma = [1.0] * 8
# Create two grids
#mu_1, sigma_1 = np.array([0.05 * i for i in range(10)]), np.array([0.5] * 10)
#mu_2, sigma_2 = np.array([1 - 0.05 * i for i in range(10)]), np.array([0.5] * 10)
mu_1, sigma_1 = np.array([-1.0] * 2 + random_mu), np.array([0.5] * 2 +
random_sigma)
mu_2, sigma_2 = np.array([1.0] * 2 + random_mu), np.array([0.5] * 2 +
random_sigma)
feat_gen_G1 = featgen.GaussianFeatureGen(mu=mu_1, sigma=sigma_1)
feat_gen_G2 = featgen.GaussianFeatureGen(mu=mu_2, sigma=sigma_2)
G1, role_id1, name = gen_syn1(feature_generator=feat_gen_G1, m=4)
G2, role_id2, name = gen_syn1(feature_generator=feat_gen_G2, m=4)
G1_size = G1.number_of_nodes()
num_roles = max(role_id1) + 1
role_id2 = [r + num_roles for r in role_id2]
label = role_id1 + role_id2
#G1 = nx.grid_graph(dim=[N,N])
#G2 = nx.grid_graph(dim=[N,N])
# Edit node ids to avoid collisions on join
g1_map = {n: i for i, n in enumerate(G1.nodes())}
G1 = nx.relabel_nodes(G1, g1_map)
g2_map = {n: i + G1_size for i, n in enumerate(G2.nodes())}
G2 = nx.relabel_nodes(G2, g2_map)
# Create node features
#com_choices_1 = [0,1]
#feat_gen_G1 = featgen.GridFeatureGen(mu=mu_1, sigma=sigma_1, com_choices=com_choices_1)
#communities_G1 = feat_gen_G1.gen_node_features(G1)
#com_choices_2 = [2,3]
#feat_gen_G2 = featgen.GridFeatureGen(mu=mu_2, sigma=sigma_2, com_choices=com_choices_2)
#communities_G2 = feat_gen_G2.gen_node_features(G2)
# Join
#n_pert_edges = int(np.log(N)) + 1;
n_pert_edges = width_basis
G = join_graph(G1, G2, n_pert_edges)
#communities = {**communities_G1, **communities_G2}
#communities = list(communities.values())
#print(label)
#name = basis_type + '_N_' + str(N) + '_classes_' + str(n_classes) + '_pert_' + str(n_pert_edges)
name = basis_type + '_' + str(width_basis) + '_' + str(
nb_shapes) + '_2comm'
return G, label, name
def gen_syn2(nb_shapes=100, width_basis=350):
""" Synthetic Graph #2:
Start with Barabasi-Albert graph and add node features indicative of a community label.
Args:
nb_shapes : The number of shapes (here 'houses') that should be added to the base graph.
width_basis : The width of the basis graph (here 'Barabasi-Albert' random graph).
Returns:
G : A networkx graph
label : Label of the nodes (determined by role_id and community)
name : A graph identifier
"""
basis_type = "ba"
random_mu = [0.0] * 8 # Change to 9
random_sigma = [1.0] * 8 # Change to 9
# # Create two grids # CHANGES BELOW was (-1) and 2 respectively (LHS)
# mu_1, sigma_1 = np.array([-2.0] * 1 + random_mu), np.array([0.5] * 1 + random_sigma)
# mu_2, sigma_2 = np.array([2.0] * 1 + random_mu), np.array([0.5] * 1 + random_sigma)
# Create two grids
mu_1, sigma_1 = np.array([-1.0] * 2 + random_mu), np.array([0.5] * 2 +
random_sigma)
mu_2, sigma_2 = np.array([1.0] * 2 + random_mu), np.array([0.5] * 2 +
random_sigma)
feat_gen_G1 = featgen.GaussianFeatureGen(mu=mu_1, sigma=sigma_1)
feat_gen_G2 = featgen.GaussianFeatureGen(mu=mu_2, sigma=sigma_2)
G1, role_id1, name = gen_syn1(feature_generator=feat_gen_G1, m=4)
G2, role_id2, name = gen_syn1(feature_generator=feat_gen_G2, m=4)
G1_size = G1.number_of_nodes()
num_roles = max(role_id1) + 1
role_id2 = [r + num_roles for r in role_id2]
label = role_id1 + role_id2
# Edit node ids to avoid collisions on join
g1_map = {n: i for i, n in enumerate(G1.nodes())}
G1 = nx.relabel_nodes(G1, g1_map)
g2_map = {n: i + G1_size for i, n in enumerate(G2.nodes())}
G2 = nx.relabel_nodes(G2, g2_map)
# Join
n_pert_edges = width_basis
G = join_graph(G1, G2, n_pert_edges)
name = basis_type + "_" + str(width_basis) + "_" + str(
nb_shapes) + "_2comm"
return G, label, name