GraphRNN2/baselines/graphvae/dataset/ENZYMES/load_data.py

import numpy as np
import networkx as nx

G = nx.Graph()
# load data
data_adj = np.loadtxt('ENZYMES_A.txt', delimiter=',').astype(int)
data_node_att = np.loadtxt('ENZYMES_node_attributes.txt', delimiter=',')
data_node_label = np.loadtxt('ENZYMES_node_labels.txt', delimiter=',').astype(int)
data_graph_indicator = np.loadtxt('ENZYMES_graph_indicator.txt', delimiter=',').astype(int)
data_graph_labels = np.loadtxt('ENZYMES_graph_labels.txt', delimiter=',').astype(int)


data_tuple = list(map(tuple, data_adj))
print(len(data_tuple))
print(data_tuple[0])

# add edges
G.add_edges_from(data_tuple)
# add node attributes
for i in range(data_node_att.shape[0]):
    G.add_node(i+1, feature = data_node_att[i])
    G.add_node(i+1, label = data_node_label[i])
G.remove_nodes_from(nx.isolates(G))

print(G.number_of_nodes())
print(G.number_of_edges())

# split into graphs
graph_num = 600
node_list = np.arange(data_graph_indicator.shape[0])+1
graphs = []
node_num_list = []
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)
    graphs.append(G_sub)
    G_sub.graph['label'] = data_graph_labels[i]
    # print('nodes', G_sub.number_of_nodes())
    # print('edges', G_sub.number_of_edges())
    # print('label', G_sub.graph)
    node_num_list.append(G_sub.number_of_nodes())
print('average', sum(node_num_list)/len(node_num_list))
print('all', len(node_num_list))
node_num_list = np.array(node_num_list)
print('selected', len(node_num_list[node_num_list>10]))
# print(graphs[0].nodes(data=True)[0][1]['feature'])
# print(graphs[0].nodes())
keys = tuple(graphs[0].nodes())
# print(nx.get_node_attributes(graphs[0], 'feature'))
dictionary = nx.get_node_attributes(graphs[0], 'feature')
# print('keys', keys)
# print('keys from dict', list(dictionary.keys()))
# print('valuse from dict', list(dictionary.values()))

features = np.zeros((len(dictionary), list(dictionary.values())[0].shape[0]))
for i in range(len(dictionary)):
    features[i,:] = list(dictionary.values())[i]
# print(features)
# print(features.shape)