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create_graphs.py
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create_graphs.py
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import networkx as nx
import numpy as np
from utils import *
from data import *
def create(args):
### load datasets
graphs=[]
# synthetic graphs
if args.graph_type=='ladder':
graphs = []
for i in range(100, 201):
graphs.append(nx.ladder_graph(i))
args.max_prev_node = 10
elif args.graph_type=='ladder_small':
graphs = []
for i in range(2, 11):
graphs.append(nx.ladder_graph(i))
args.max_prev_node = 10
elif args.graph_type=='tree':
graphs = []
for i in range(2,5):
for j in range(3,5):
graphs.append(nx.balanced_tree(i,j))
args.max_prev_node = 256
elif args.graph_type=='caveman':
# graphs = []
# for i in range(5,10):
# for j in range(5,25):
# for k in range(5):
# graphs.append(nx.relaxed_caveman_graph(i, j, p=0.1))
graphs = []
for i in range(2, 3):
for j in range(30, 81):
for k in range(10):
graphs.append(caveman_special(i,j, p_edge=0.3))
args.max_prev_node = 100
elif args.graph_type=='caveman_small':
# graphs = []
# for i in range(2,5):
# for j in range(2,6):
# for k in range(10):
# graphs.append(nx.relaxed_caveman_graph(i, j, p=0.1))
graphs = []
for i in range(2, 3):
for j in range(6, 11):
for k in range(20):
graphs.append(caveman_special(i, j, p_edge=0.8)) # default 0.8
args.max_prev_node = 20
elif args.graph_type=='caveman_small_single':
# graphs = []
# for i in range(2,5):
# for j in range(2,6):
# for k in range(10):
# graphs.append(nx.relaxed_caveman_graph(i, j, p=0.1))
graphs = []
for i in range(2, 3):
for j in range(8, 9):
for k in range(100):
graphs.append(caveman_special(i, j, p_edge=0.5))
args.max_prev_node = 20
elif args.graph_type.startswith('community'):
num_communities = int(args.graph_type[-1])
print('Creating dataset with ', num_communities, ' communities')
c_sizes = np.random.choice([12, 13, 14, 15, 16, 17], num_communities)
#c_sizes = [15] * num_communities
for k in range(3000):
graphs.append(n_community(c_sizes, p_inter=0.01))
args.max_prev_node = 80
elif args.graph_type=='grid':
graphs = []
for i in range(10,20):
for j in range(10,20):
graphs.append(nx.grid_2d_graph(i,j))
args.max_prev_node = 40
elif args.graph_type=='grid_small':
graphs = []
for i in range(2,5):
for j in range(2,6):
graphs.append(nx.grid_2d_graph(i,j))
args.max_prev_node = 15
elif args.graph_type=='barabasi':
graphs = []
for i in range(100,200):
for j in range(4,5):
for k in range(5):
graphs.append(nx.barabasi_albert_graph(i,j))
args.max_prev_node = 130
elif args.graph_type=='barabasi_small':
graphs = []
for i in range(4,21):
for j in range(3,4):
for k in range(10):
graphs.append(nx.barabasi_albert_graph(i,j))
args.max_prev_node = 20
elif args.graph_type=='grid_big':
graphs = []
for i in range(36, 46):
for j in range(36, 46):
graphs.append(nx.grid_2d_graph(i, j))
args.max_prev_node = 90
elif 'barabasi_noise' in args.graph_type:
graphs = []
for i in range(100,101):
for j in range(4,5):
for k in range(500):
graphs.append(nx.barabasi_albert_graph(i,j))
graphs = perturb_new(graphs,p=args.noise/10.0)
args.max_prev_node = 99
# real graphs
elif args.graph_type == 'enzymes':
graphs= Graph_load_batch(min_num_nodes=10, name='ENZYMES')
args.max_prev_node = 25
elif args.graph_type == 'enzymes_small':
graphs_raw = Graph_load_batch(min_num_nodes=10, name='ENZYMES')
graphs = []
for G in graphs_raw:
if G.number_of_nodes()<=20:
graphs.append(G)
args.max_prev_node = 15
elif args.graph_type == 'protein':
graphs = Graph_load_batch(min_num_nodes=20, name='PROTEINS_full')
args.max_prev_node = 80
elif args.graph_type == 'DD':
graphs = Graph_load_batch(min_num_nodes=100, max_num_nodes=500, name='DD',node_attributes=False,graph_labels=True)
args.max_prev_node = 230
elif args.graph_type == 'citeseer':
_, _, G = Graph_load(dataset='citeseer')
G = max(nx.connected_component_subgraphs(G), key=len)
G = nx.convert_node_labels_to_integers(G)
graphs = []
for i in range(G.number_of_nodes()):
G_ego = nx.ego_graph(G, i, radius=3)
if G_ego.number_of_nodes() >= 50 and (G_ego.number_of_nodes() <= 400):
graphs.append(G_ego)
args.max_prev_node = 250
elif args.graph_type == 'citeseer_small':
_, _, G = Graph_load(dataset='citeseer')
G = max(nx.connected_component_subgraphs(G), key=len)
G = nx.convert_node_labels_to_integers(G)
graphs = []
for i in range(G.number_of_nodes()):
G_ego = nx.ego_graph(G, i, radius=1)
if (G_ego.number_of_nodes() >= 4) and (G_ego.number_of_nodes() <= 20):
graphs.append(G_ego)
shuffle(graphs)
graphs = graphs[0:200]
args.max_prev_node = 15
return graphs