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import math
import os.path as osp
import time
from itertools import chain
import numpy as np
import torch
import torch.nn.functional as F
from scipy.sparse.csgraph import shortest_path
from sklearn.metrics import roc_auc_score
from torch.nn import BCEWithLogitsLoss, Conv1d, MaxPool1d, ModuleList
from torch_geometric.data import Data, InMemoryDataset
from torch_geometric.datasets import Planetoid
from torch_geometric.loader import DataLoader
from torch_geometric.nn import MLP, GCNConv, SortAggregation
from torch_geometric.transforms import RandomLinkSplit
from torch_geometric.utils import k_hop_subgraph, to_scipy_sparse_matrix
class SEALDataset(InMemoryDataset):
def __init__(self, dataset, num_hops, split='train'):
self._data = dataset[0]
self.num_hops = num_hops
super().__init__(dataset.root)
index = ['train', 'val', 'test'].index(split)
self.load(self.processed_paths[index])
@property
def processed_file_names(self):
return ['SEAL_train_data.pt', 'SEAL_val_data.pt', 'SEAL_test_data.pt']
def process(self):
transform = RandomLinkSplit(num_val=0.05, num_test=0.1,
is_undirected=True, split_labels=True)
train_data, val_data, test_data = transform(self._data)
self._max_z = 0
# Collect a list of subgraphs for training, validation and testing:
train_pos_data_list = self.extract_enclosing_subgraphs(
train_data.edge_index, train_data.pos_edge_label_index, 1)
train_neg_data_list = self.extract_enclosing_subgraphs(
train_data.edge_index, train_data.neg_edge_label_index, 0)
val_pos_data_list = self.extract_enclosing_subgraphs(
val_data.edge_index, val_data.pos_edge_label_index, 1)
val_neg_data_list = self.extract_enclosing_subgraphs(
val_data.edge_index, val_data.neg_edge_label_index, 0)
test_pos_data_list = self.extract_enclosing_subgraphs(
test_data.edge_index, test_data.pos_edge_label_index, 1)
test_neg_data_list = self.extract_enclosing_subgraphs(
test_data.edge_index, test_data.neg_edge_label_index, 0)
# Convert node labeling to one-hot features.
for data in chain(train_pos_data_list, train_neg_data_list,
val_pos_data_list, val_neg_data_list,
test_pos_data_list, test_neg_data_list):
# We solely learn links from structure, dropping any node features:
data.x = F.one_hot(data.z, self._max_z + 1).to(torch.float)
train_data_list = train_pos_data_list + train_neg_data_list
self.save(train_data_list, self.processed_paths[0])
val_data_list = val_pos_data_list + val_neg_data_list
self.save(val_data_list, self.processed_paths[1])
test_data_list = test_pos_data_list + test_neg_data_list
self.save(test_data_list, self.processed_paths[2])
def extract_enclosing_subgraphs(self, edge_index, edge_label_index, y):
data_list = []
for src, dst in edge_label_index.t().tolist():
sub_nodes, sub_edge_index, mapping, _ = k_hop_subgraph(
[src, dst], self.num_hops, edge_index, relabel_nodes=True)
src, dst = mapping.tolist()
# Remove target link from the subgraph.
mask1 = (sub_edge_index[0] != src) | (sub_edge_index[1] != dst)
mask2 = (sub_edge_index[0] != dst) | (sub_edge_index[1] != src)
sub_edge_index = sub_edge_index[:, mask1 & mask2]
# Calculate node labeling.
z = self.drnl_node_labeling(sub_edge_index, src, dst,
num_nodes=sub_nodes.size(0))
data = Data(x=self._data.x[sub_nodes], z=z,
edge_index=sub_edge_index, y=y)
data_list.append(data)
return data_list
def drnl_node_labeling(self, edge_index, src, dst, num_nodes=None):
# Double-radius node labeling (DRNL).
src, dst = (dst, src) if src > dst else (src, dst)
adj = to_scipy_sparse_matrix(edge_index, num_nodes=num_nodes).tocsr()
idx = list(range(src)) + list(range(src + 1, adj.shape[0]))
adj_wo_src = adj[idx, :][:, idx]
idx = list(range(dst)) + list(range(dst + 1, adj.shape[0]))
adj_wo_dst = adj[idx, :][:, idx]
dist2src = shortest_path(adj_wo_dst, directed=False, unweighted=True,
indices=src)
dist2src = np.insert(dist2src, dst, 0, axis=0)
dist2src = torch.from_numpy(dist2src)
dist2dst = shortest_path(adj_wo_src, directed=False, unweighted=True,
indices=dst - 1)
dist2dst = np.insert(dist2dst, src, 0, axis=0)
dist2dst = torch.from_numpy(dist2dst)
dist = dist2src + dist2dst
dist_over_2, dist_mod_2 = dist // 2, dist % 2
z = 1 + torch.min(dist2src, dist2dst)
z += dist_over_2 * (dist_over_2 + dist_mod_2 - 1)
z[src] = 1.
z[dst] = 1.
z[torch.isnan(z)] = 0.
self._max_z = max(int(z.max()), self._max_z)
return z.to(torch.long)
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'Planetoid')
dataset = Planetoid(path, name='Cora')
train_dataset = SEALDataset(dataset, num_hops=2, split='train')
val_dataset = SEALDataset(dataset, num_hops=2, split='val')
test_dataset = SEALDataset(dataset, num_hops=2, split='test')
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32)
test_loader = DataLoader(test_dataset, batch_size=32)
class DGCNN(torch.nn.Module):
def __init__(self, hidden_channels, num_layers, GNN=GCNConv, k=0.6):
super().__init__()
if k < 1: # Transform percentile to number.
num_nodes = sorted([data.num_nodes for data in train_dataset])
k = num_nodes[int(math.ceil(k * len(num_nodes))) - 1]
k = int(max(10, k))
self.convs = ModuleList()
self.convs.append(GNN(train_dataset.num_features, hidden_channels))
for i in range(0, num_layers - 1):
self.convs.append(GNN(hidden_channels, hidden_channels))
self.convs.append(GNN(hidden_channels, 1))
conv1d_channels = [16, 32]
total_latent_dim = hidden_channels * num_layers + 1
conv1d_kws = [total_latent_dim, 5]
self.conv1 = Conv1d(1, conv1d_channels[0], conv1d_kws[0],
conv1d_kws[0])
self.pool = SortAggregation(k)
self.maxpool1d = MaxPool1d(2, 2)
self.conv2 = Conv1d(conv1d_channels[0], conv1d_channels[1],
conv1d_kws[1], 1)
dense_dim = int((k - 2) / 2 + 1)
dense_dim = (dense_dim - conv1d_kws[1] + 1) * conv1d_channels[1]
self.mlp = MLP([dense_dim, 128, 1], dropout=0.5, norm=None)
def forward(self, x, edge_index, batch):
xs = [x]
for conv in self.convs:
xs += [conv(xs[-1], edge_index).tanh()]
x = torch.cat(xs[1:], dim=-1)
# Global pooling.
x = self.pool(x, batch)
x = x.unsqueeze(1) # [num_graphs, 1, k * hidden]
x = self.conv1(x).relu()
x = self.maxpool1d(x)
x = self.conv2(x).relu()
x = x.view(x.size(0), -1) # [num_graphs, dense_dim]
return self.mlp(x)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = DGCNN(hidden_channels=32, num_layers=3).to(device)
optimizer = torch.optim.Adam(params=model.parameters(), lr=0.0001)
criterion = BCEWithLogitsLoss()
def train():
model.train()
total_loss = 0
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
out = model(data.x, data.edge_index, data.batch)
loss = criterion(out.view(-1), data.y.to(torch.float))
loss.backward()
optimizer.step()
total_loss += float(loss) * data.num_graphs
return total_loss / len(train_dataset)
@torch.no_grad()
def test(loader):
model.eval()
y_pred, y_true = [], []
for data in loader:
data = data.to(device)
logits = model(data.x, data.edge_index, data.batch)
y_pred.append(logits.view(-1).cpu())
y_true.append(data.y.view(-1).cpu().to(torch.float))
return roc_auc_score(torch.cat(y_true), torch.cat(y_pred))
times = []
best_val_auc = test_auc = 0
for epoch in range(1, 51):
start = time.time()
loss = train()
val_auc = test(val_loader)
if val_auc > best_val_auc:
best_val_auc = val_auc
test_auc = test(test_loader)
print(f'Epoch: {epoch:02d}, Loss: {loss:.4f}, Val: {val_auc:.4f}, '
f'Test: {test_auc:.4f}')
times.append(time.time() - start)
print(f"Median time per epoch: {torch.tensor(times).median():.4f}s")
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