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data_generators.py
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data_generators.py
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import json
import logging
import os
import pickle
import networkx as nx
import numpy as np
import scipy.sparse as sp
import argparse
# -------- Generate community graphs --------
def n_community(num_communities, max_nodes, p_inter=0.05):
# -------- From Niu et al. (2020) --------
assert num_communities > 1
one_community_size = max_nodes // num_communities
c_sizes = [one_community_size] * num_communities
total_nodes = one_community_size * num_communities
p_make_a_bridge = p_inter * 2 / ((num_communities - 1) * one_community_size)
print(num_communities, total_nodes, end=' ')
graphs = [nx.gnp_random_graph(c_sizes[i], 0.7, seed=i) for i in range(len(c_sizes))]
G = nx.disjoint_union_all(graphs)
communities = list(G.subgraph(c) for c in nx.connected_components(G))
add_edge = 0
for i in range(len(communities)):
subG1 = communities[i]
nodes1 = list(subG1.nodes())
for j in range(i + 1, len(communities)): # loop for C_M^2 times
subG2 = communities[j]
nodes2 = list(subG2.nodes())
has_inter_edge = False
for n1 in nodes1: # loop for N times
for n2 in nodes2: # loop for N times
if np.random.rand() < p_make_a_bridge:
G.add_edge(n1, n2)
has_inter_edge = True
add_edge += 1
if not has_inter_edge:
G.add_edge(nodes1[0], nodes2[0])
add_edge += 1
print('connected comp: ', len( list(G.subgraph(c) for c in nx.connected_components(G)) ),
'add edges: ', add_edge)
print(G.number_of_edges())
return G
NAME_TO_NX_GENERATOR = {
'community': n_community,
'grid': nx.generators.grid_2d_graph,
# -------- Additional datasets --------
'gnp': nx.generators.fast_gnp_random_graph, # fast_gnp_random_graph(n, p, seed=None, directed=False)
'ba': nx.generators.barabasi_albert_graph, # barabasi_albert_graph(n, m, seed=None)
'pow_law': lambda **kwargs: nx.configuration_model(nx.generators.random_powerlaw_tree_sequence(**kwargs, gamma=3,
tries=2000)),
'except_deg': lambda **kwargs: nx.expected_degree_graph(**kwargs, selfloops=False),
'cycle': nx.cycle_graph,
'c_l': nx.circular_ladder_graph,
'lobster': nx.random_lobster
}
class GraphGenerator:
def __init__(self, graph_type='grid', possible_params_dict=None, corrupt_func=None):
if possible_params_dict is None:
possible_params_dict = {}
assert isinstance(possible_params_dict, dict)
self.count = {k: 0 for k in possible_params_dict}
self.possible_params = possible_params_dict
self.corrupt_func = corrupt_func
self.nx_generator = NAME_TO_NX_GENERATOR[graph_type]
def __call__(self):
params = {}
for k, v_list in self.possible_params.items():
params[k] = np.random.choice(v_list)
graph = self.nx_generator(**params)
graph = nx.relabel.convert_node_labels_to_integers(graph)
if self.corrupt_func is not None:
graph = self.corrupt_func(self.corrupt_func)
return graph
# -------- Generate synthetic graphs --------
def gen_graph_list(graph_type='grid', possible_params_dict=None, corrupt_func=None, length=1024, save_dir=None,
file_name=None, max_node=None, min_node=None):
params = locals()
logging.info('gen data: ' + json.dumps(params))
if file_name is None:
file_name = graph_type + '_' + str(length)
file_path = os.path.join(save_dir, file_name)
graph_generator = GraphGenerator(graph_type=graph_type,
possible_params_dict=possible_params_dict,
corrupt_func=corrupt_func)
graph_list = []
i = 0
max_N = 0
while i < length:
graph = graph_generator()
if max_node is not None and graph.number_of_nodes() > max_node:
continue
if min_node is not None and graph.number_of_nodes() < min_node:
continue
print(i, graph.number_of_nodes(), graph.number_of_edges())
max_N = max(max_N, graph.number_of_nodes())
if graph.number_of_nodes() <= 1:
continue
graph_list.append(graph)
i += 1
if save_dir is not None:
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
with open(file_path + '.pkl', 'wb') as f:
pickle.dump(obj=graph_list, file=f, protocol=pickle.HIGHEST_PROTOCOL)
with open(file_path + '.txt', 'w') as f:
f.write(json.dumps(params))
f.write(f'max node number: {max_N}')
print(max_N)
return graph_list
def load_dataset(data_dir='data', file_name=None, need_set=False):
file_path = os.path.join(data_dir, file_name)
with open(file_path + '.pkl', 'rb') as f:
graph_list = pickle.load(f)
return graph_list
# -------- load ENZYMES, PROTEIN and DD dataset --------
def graph_load_batch(min_num_nodes=20, max_num_nodes=1000, name='ENZYMES', node_attributes=True, graph_labels=True):
"""
load many graphs, e.g. enzymes
:return: a list of graphs
"""
print('Loading graph dataset: ' + str(name))
G = nx.Graph()
# -------- load data --------
path = 'dataset/' + name + '/'
data_adj = np.loadtxt(path + name + '_A.txt', delimiter=',').astype(int)
data_node_att = []
if node_attributes:
data_node_att = np.loadtxt(path + name + '_node_attributes.txt', delimiter=',')
data_node_label = np.loadtxt(path + name + '_node_labels.txt', delimiter=',').astype(int)
data_graph_indicator = np.loadtxt(path + name + '_graph_indicator.txt', delimiter=',').astype(int)
if graph_labels:
data_graph_labels = np.loadtxt(path + name + '_graph_labels.txt', delimiter=',').astype(int)
data_tuple = list(map(tuple, data_adj))
# -------- add edges --------
G.add_edges_from(data_tuple)
# -------- add node attributes --------
for i in range(data_node_label.shape[0]):
if node_attributes:
G.add_node(i + 1, feature=data_node_att[i])
G.add_node(i + 1, label=data_node_label[i])
G.remove_nodes_from(list(nx.isolates(G)))
print(G.number_of_nodes())
print(G.number_of_edges())
# -------- split into graphs --------
graph_num = data_graph_indicator.max()
node_list = np.arange(data_graph_indicator.shape[0]) + 1
graphs = []
max_nodes = 0
for i in range(graph_num):
# -------- find the nodes for each graph --------
nodes = node_list[data_graph_indicator == i + 1]
G_sub = G.subgraph(nodes)
if graph_labels:
G_sub.graph['label'] = data_graph_labels[i]
if min_num_nodes <= G_sub.number_of_nodes() <= max_num_nodes:
graphs.append(G_sub)
if G_sub.number_of_nodes() > max_nodes:
max_nodes = G_sub.number_of_nodes()
print(f'Graphs loaded, total num: {len(graphs)}')
return graphs
def parse_index_file(filename):
index = []
for line in open(filename):
index.append(int(line.strip()))
return index
# -------- load cora, citeseer and pubmed dataset --------
def graph_load(dataset='cora'):
"""
Load a single graph dataset
:param dataset: dataset name
:return:
"""
names = ['x', 'tx', 'allx', 'graph']
objects = []
for i in range(len(names)):
load = pickle.load(open("dataset/ind.{}.{}".format(dataset, names[i]), 'rb'), encoding='latin1')
objects.append(load)
x, tx, allx, graph = tuple(objects)
test_idx_reorder = parse_index_file("dataset/ind.{}.test.index".format(dataset))
test_idx_range = np.sort(test_idx_reorder)
if dataset == 'citeseer':
# -------- Fix citeseer dataset (there are some isolated nodes in the graph) --------
# -------- Find isolated nodes, add them as zero-vecs into the right position --------
test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder) + 1)
tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1]))
tx_extended[test_idx_range - min(test_idx_range), :] = tx
tx = tx_extended
features = sp.vstack((allx, tx)).tolil()
features[test_idx_reorder, :] = features[test_idx_range, :]
G = nx.from_dict_of_lists(graph)
return features, G
def citeseer_ego(radius=3, node_min=50, node_max=400):
_, G = graph_load(dataset='citeseer')
G = max([G.subgraph(c) for c in nx.connected_components(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=radius)
assert isinstance(G_ego, nx.Graph)
if G_ego.number_of_nodes() >= node_min and (G_ego.number_of_nodes() <= node_max):
G_ego.remove_edges_from(nx.selfloop_edges(G_ego))
graphs.append(G_ego)
return graphs
def save_dataset(data_dir, graphs, save_name):
if not os.path.isdir(data_dir):
os.makedirs(data_dir)
file_path = os.path.join('data', save_name)
print(save_name, len(graphs))
with open(file_path + '.pkl', 'wb') as f:
pickle.dump(obj=graphs, file=f, protocol=pickle.HIGHEST_PROTOCOL)
with open(file_path + '.txt', 'w') as f:
f.write(save_name + '\n')
f.write(str(len(graphs)))
# -------- Generate datasets --------
def generate_dataset(data_dir='data', dataset='community_small'):
if dataset == 'community_small':
res_graph_list = gen_graph_list(graph_type='community', possible_params_dict={
'num_communities': [2],
'max_nodes': np.arange(12, 21).tolist()},
corrupt_func=None, length=100, save_dir=data_dir, file_name=dataset)
elif dataset == 'grid':
res_graph_list = gen_graph_list(graph_type='grid', possible_params_dict={
'm': np.arange(10, 20).tolist(),
'n': np.arange(10, 20).tolist()},
corrupt_func=None, length=100, save_dir=data_dir, file_name=dataset)
elif dataset == 'ego_small':
graphs = citeseer_ego(radius=1, node_min=4, node_max=18)[:200]
save_dataset(data_dir, graphs, dataset)
print(max([g.number_of_nodes() for g in graphs]))
elif dataset == 'ENZYMES':
graphs = graph_load_batch(min_num_nodes=10, max_num_nodes=1000, name=dataset,
node_attributes=False, graph_labels=True)
save_dataset(data_dir, graphs, dataset)
print(max([g.number_of_nodes() for g in graphs]))
else:
raise NotImplementedError(f'Dataset {datset} not supproted.')
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Generate dataset')
parser.add_argument('--data-dir', type=str, default='data', help='directory to save the generated dataset')
parser.add_argument('--dataset', type=str, default='community_small', help='dataset to generate',
choices=['ego_small', 'community_small', 'ENZYMES', 'grid'])
args = parser.parse_args()
generate_dataset(args.data_dir, args.dataset)