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preprocessGAE.py
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preprocessGAE.py
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import numpy as np
import scipy.sparse as sp
def sparse_to_tuple(sparse_mx):
if not sp.isspmatrix_coo(sparse_mx):
sparse_mx = sparse_mx.tocoo()
coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose()
values = sparse_mx.data
shape = sparse_mx.shape
return coords, values, shape
def preprocess_graph(adj):
adj = sp.coo_matrix(adj)
adj_ = adj + sp.eye(adj.shape[0])
rowsum = np.array(adj_.sum(1))
degree_mat_inv_sqrt = sp.diags(np.power(rowsum, -0.5).flatten())
adj_normalized = adj_.dot(degree_mat_inv_sqrt).transpose().dot(degree_mat_inv_sqrt).tocoo()
return sparse_to_tuple(adj_normalized)
def construct_feed_dict(adj_normalized, adj, features, placeholders):
# construct feed dictionary
feed_dict = dict()
feed_dict.update({placeholders['features']: features})
feed_dict.update({placeholders['adj']: adj_normalized})
feed_dict.update({placeholders['adj_orig']: adj})
return feed_dict
def mask_test_edges(adj):
# Function to build test set with 2% positive links
# Remove diagonal elements
adj = adj - sp.dia_matrix((adj.diagonal()[np.newaxis, :], [0]), shape=adj.shape)
adj.eliminate_zeros()
adj_triu = sp.triu(adj)
adj_tuple = sparse_to_tuple(adj_triu)
edges = adj_tuple[0]
edges_all = sparse_to_tuple(adj)[0]
num_test = int(np.floor(edges.shape[0] / 10.))
num_val = int(np.floor(edges.shape[0] / 20.))
all_edge_idx = range(edges.shape[0])
np.random.shuffle(all_edge_idx)
val_edge_idx = all_edge_idx[:num_val]
test_edge_idx = all_edge_idx[num_val:(num_val + num_test)]
test_edges = edges[test_edge_idx]
val_edges = edges[val_edge_idx]
train_edges = np.delete(edges, np.hstack([test_edge_idx, val_edge_idx]), axis=0)
def ismember(a, b):
rows_close = np.all((a - b[:, None]) == 0, axis=-1)
return np.any(rows_close)
test_edges_false = []
while len(test_edges_false) < len(test_edges):
n_rnd = len(test_edges) - len(test_edges_false)
rnd = np.random.randint(0, adj.shape[0], size=2 * n_rnd)
idxs_i = rnd[:n_rnd]
idxs_j = rnd[n_rnd:]
for i in range(n_rnd):
idx_i = idxs_i[i]
idx_j = idxs_j[i]
if idx_i == idx_j:
continue
if ismember([idx_i, idx_j], edges_all):
continue
if test_edges_false:
if ismember([idx_j, idx_i], np.array(test_edges_false)):
continue
if ismember([idx_i, idx_j], np.array(test_edges_false)):
continue
test_edges_false.append([idx_i, idx_j])
val_edges_false = []
while len(val_edges_false) < len(val_edges):
n_rnd = len(val_edges) - len(val_edges_false)
rnd = np.random.randint(0, adj.shape[0], size=2 * n_rnd)
idxs_i = rnd[:n_rnd]
idxs_j = rnd[n_rnd:]
for i in range(n_rnd):
idx_i = idxs_i[i]
idx_j = idxs_j[i]
if idx_i == idx_j:
continue
if ismember([idx_i, idx_j], train_edges):
continue
if ismember([idx_j, idx_i], train_edges):
continue
if ismember([idx_i, idx_j], val_edges):
continue
if ismember([idx_j, idx_i], val_edges):
continue
if val_edges_false:
if ismember([idx_j, idx_i], np.array(val_edges_false)):
continue
if ismember([idx_i, idx_j], np.array(val_edges_false)):
continue
val_edges_false.append([idx_i, idx_j])
# Re-build adj matrix
data = np.ones(train_edges.shape[0])
adj_train = sp.csr_matrix((data, (train_edges[:, 0], train_edges[:, 1])), shape=adj.shape)
adj_train = adj_train + adj_train.T
return adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false