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preprocessing.py
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import numpy as np
import scipy.sparse as sp
np.random.seed(159)
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), adj_normalized
def preprocess_graph_e(adj):
adj = sp.coo_matrix(adj)
adj_ = 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()
sym_l = sp.eye(adj_normalized.shape[0]) - adj_normalized
true_a = adj_normalized + 0.5 * sym_l.dot(sym_l) - 1/6 * sym_l.dot(sym_l).dot(sym_l)
return sparse_to_tuple(true_a), true_a
def construct_feed_dict(adj_normalized, adj, features,clean_mask, noised_mask,noised_num, 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})
feed_dict.update({placeholders['clean_mask']: clean_mask})
feed_dict.update({placeholders['noised_mask']: noised_mask})
feed_dict.update({placeholders['noised_num']: noised_num})
# feed_dict.update({placeholders['node_ids']: node_ids})
# feed_dict.update({placeholders['node_labels']: node_labels})
#feed_dict.update({placeholders['comm_label']: comm_label})
return feed_dict
def construct_feed_dict_trained(adj_normalized, adj, features, placeholders):
feed_dict = dict()
feed_dict.update({placeholders['features']["indices"]: features[0]})
feed_dict.update({placeholders['features']["values"]: features[1]})
feed_dict.update({placeholders['features']["shape"]: features[2]})
feed_dict.update({placeholders['adj']["indices"]: adj_normalized[0]})
feed_dict.update({placeholders['adj']["values"]: adj_normalized[1]})
feed_dict.update({placeholders['adj']["shape"]: adj_normalized[2]})
feed_dict.update({placeholders['adj_orig']["indices"]: adj[0]})
feed_dict.update({placeholders['adj_orig']["values"]: adj[1]})
feed_dict.update({placeholders['adj_orig']["shape"]: adj[2]})
#feed_dict.update({placeholders['comm_label']: comm_label})
return feed_dict
def mask_test_edges(adj):
# Function to build test set with 10% positive links
# NOTE: Splits are randomized and results might slightly deviate from reported numbers in the paper.
# TODO: Clean up.
# Remove diagonal elements
adj = adj - sp.dia_matrix((adj.diagonal()[np.newaxis, :], [0]), shape=adj.shape)
adj.eliminate_zeros()
# Check that diag is zero:
assert np.diag(adj.todense()).sum() == 0
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 = list(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, tol=5):
rows_close = np.all(np.round(a - b[:, None], tol) == 0, axis=-1)
return np.any(rows_close)
test_edges_false = []
while len(test_edges_false) < len(test_edges):
idx_i = np.random.randint(0, adj.shape[0])
idx_j = np.random.randint(0, adj.shape[0])
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):
idx_i = np.random.randint(0, adj.shape[0])
idx_j = np.random.randint(0, adj.shape[0])
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])
assert ~ismember(test_edges_false, edges_all)
assert ~ismember(val_edges_false, edges_all)
assert ~ismember(val_edges, train_edges)
assert ~ismember(test_edges, train_edges)
assert ~ismember(val_edges, test_edges)
data = np.ones(train_edges.shape[0])
# Re-build adj matrix
adj_train = sp.csr_matrix((data, (train_edges[:, 0], train_edges[:, 1])), shape=adj.shape)
adj_train = adj_train + adj_train.T
# NOTE: these edge lists only contain single direction of edge!
return adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false
# def get_target_nodes_and_comm_labels(target_str):
# out_list = []
# target_list = target_str.split(",")
# out_list.append([9,10])
# out_list.append([34,35])
# return out_list