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create_graphs.py
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create_graphs.py
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import random
import networkx as nx
import numpy as np
import os
import pickle as pkl
from utils import caveman_special, n_community, perturb_new, Graph_load_batch, Graph_load
def generate_ladder_graphs(min_size:int = 100, max_size:int = 201):
return [nx.ladder_graph(i) for i in range(min_size, max_size)]
def create(args):
# synthetic graphs
if args.graph_type == "ladder":
graphs = generate_ladder_graphs(100, 201)
args.max_prev_node = 10
elif args.graph_type == "ladder_small":
graphs = generate_ladder_graphs(2, 11)
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(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, 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, 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)
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)
random.shuffle(graphs)
graphs = graphs[0:200]
args.max_prev_node = 15
elif args.graph_type == "collagen":
graphs = []
for fname in os.listdir(os.path.join("./", "dataset", "Collagen")):
base, ext = os.path.splitext(fname)
print(fname, base, ext)
if ext != ".pkl":
continue
with open(os.path.join("./", "dataset", "Collagen", fname), "rb") as fi:
graphs.append(pkl.load(fi))
args.max_num_node = 800
args.max_prev_node = 8
elif args.graph_type == "edge_graphs":
graphs = []
for fname in os.listdir(os.path.join("./", "dataset", "edge_graphs")):
base, ext = os.path.splitext(fname)
print(fname, base, ext)
if ext != ".pkl":
continue
with open(os.path.join("./", "dataset", "edge_graphs", fname), "rb") as fi:
graphs.append(pkl.load(fi))
args.max_num_node = max(len(graph) for graph in graphs)
args.max_prev_node = 8
elif args.graph_type == "ring_graphs":
graphs = []
for fname in os.listdir(os.path.join("./", "dataset", "ring_graphs")):
base, ext = os.path.splitext(fname)
print(fname, base, ext)
if ext != ".pkl":
continue
with open(os.path.join("./", "dataset", "ring_graphs", fname), "rb") as fi:
graphs.append(pkl.load(fi))
args.max_num_node = max(len(graph) for graph in graphs)
args.max_prev_node = 20
random.shuffle(graphs)
return graphs