Cora数据集
介绍
下载地址：https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz
Cora数据集由深度学习论文组成，论文表示为节点，论文之间的引用关系表示为节点之间的边，每篇论文引用或被至少一篇其他论文引用，不存在孤立节点。
论文被分为以下七类之一:
- Case_Based
- Genetic_Algorithms
- Neural_Networks
- Probabilistic_Methods
- Reinforcement_Learning
- Rule_Learning
- Theory
数据集组成
cora.cites --论文之间的引用情况（边）
cora.content --论文内容（节点特征+标签）
节点特征使用筛选后单词的one-hot编码，若某词出现在该论文中，对应位置置1
读取数据集
path = "data/cora/"
cites = path + "cora.cites"
content = path + "cora.content"
index_dict = dict()
label_to_index = dict()
features = []
labels = []
edge_index = []
with open(content,"r") as f:
nodes = f.readlines()
for node in nodes:
node_info = node.split()
index_dict[int(node_info[0])] = len(index_dict)
features.append([int(i) for i in node_info[1:-1]])
label_str = node_info[-1]
if(label_str not in label_to_index.keys()):
label_to_index[label_str] = len(label_to_index)
labels.append(label_to_index[label_str])
with open(cites,"r") as f:
edges = f.readlines()
for edge in edges:
start, end = edge.split()
edge_index.append([index_dict[int(start)],index_dict[int(end)]])
edge_index.append([index_dict[int(end)],index_dict[int(start)]])
labels = torch.LongTensor(labels)
features = torch.FloatTensor(features)
edge_index = torch.LongTensor(edge_index)
两层GCN的网络结构
class GCNNet(torch.nn.Module):
def __init__(self, num_feature, num_label):
super(GCNNet,self).__init__()
self.GCN1 = GCNConv(num_feature, 16)
self.GCN2 = GCNConv(16, num_label)
self.dropout = torch.nn.Dropout(p=0.5)
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = self.GCN1(x, edge_index)
x = F.relu(x)
x = self.dropout(x)
x = self.GCN2(x, edge_index)
return F.log_softmax(x, dim=1)
两层GAT的网络结构
class GATNet(torch.nn.Module):
def __init__(self, num_feature, num_label):
super(GATNet,self).__init__()
self.GAT1 = GATConv(num_feature, 8, heads = 8, concat = True, dropout = 0.6)
self.GAT2 = GATConv(8*8, num_label, dropout = 0.6)
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = self.GAT1(x, edge_index)
x = F.relu(x)
x = self.GAT2(x, edge_index)
return F.log_softmax(x, dim=1)
固定随机种子
seed = 1234
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
划分训练/验证/测试集，实例化Data对象用于存储
Data对象的使用方法在源码仓库里也有，地址放在文末链接。
mask = torch.randperm(len(index_dict))
train_mask = mask[:140]
val_mask = mask[140:640]
test_mask = mask[1708:2708]
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
cora = Data(x = features, edge_index = edge_index.t().contiguous(), y = labels).to(device)
训练网络
model = GATNet(features.shape[1], len(label_to_index)).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
for epoch in range(200):
optimizer.zero_grad()
out = model(cora)
loss = F.nll_loss(out[train_mask], cora.y[train_mask])
print('epoch: %d loss: %.4f' %(epoch, loss))
loss.backward()
optimizer.step()
if((epoch + 1)% 10 == 0):
model.eval()
_, pred = model(cora).max(dim=1)
correct = int(pred[test_mask].eq(cora.y[test_mask]).sum().item())
acc = correct / len(test_mask)
print('Accuracy: {:.4f}'.format(acc))
model.train()
epoch: 0 loss: 1.9512
epoch: 1 loss: 1.7456
epoch: 2 loss: 1.5565
epoch: 3 loss: 1.3312
epoch: 4 loss: 1.1655
epoch: 5 loss: 0.9590
epoch: 6 loss: 0.8127
epoch: 7 loss: 0.7368
epoch: 8 loss: 0.6223
epoch: 9 loss: 0.6382
Accuracy: 0.8180
...
epoch: 190 loss: 0.4079
epoch: 191 loss: 0.2836
epoch: 192 loss: 0.3000
epoch: 193 loss: 0.2390
epoch: 194 loss: 0.2207
epoch: 195 loss: 0.2316
epoch: 196 loss: 0.2994
epoch: 197 loss: 0.2480
epoch: 198 loss: 0.2349
epoch: 199 loss: 0.2657
Accuracy: 0.8290
t-SNE做图观察特征空间
ts = TSNE(n_components=2)
ts.fit_transform(out[test_mask].to('cpu').detach().numpy())
x = ts.embedding_
y = cora.y[test_mask].to('cpu').detach().numpy()
xi = []
for i in range(7):
xi.append(x[np.where(y==i)])
colors = ['mediumblue','green','red','yellow','cyan','mediumvioletred','mediumspringgreen']
plt.figure(figsize=(8, 6))
for i in range(7):
plt.scatter(xi[i][:,0],xi[i][:,1],s=30,color=colors[i],marker='+',alpha=1)
参考文献
GCN论文
GAT论文
pytorch-geometric官方文档
源码地址
https://gitee.com/swy9834/gnnlab
作者用以存放基于pytorch-geometric的GNN学习代码，随缘更新。
Original: https://blog.csdn.net/qq_20805805/article/details/117720945
Author: 啊喔呃鸭
Title: GCN、GAT实现Cora数据集节点分类（pytorch-geometric框架）
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