大规模图数据思路代码

import torch
import torch.nn.functional as F
from torch_geometric.datasets import Planetoid
from torch_geometric.nn import GCNConv
from torch_geometric.loader import NeighborSampler

# 定义图神经网络模型
class GCN(torch.nn.Module):
    def __init__(self, num_features, num_classes):
        super(GCN, self).__init__()
        self.conv1 = GCNConv(num_features, 16)
        self.conv2 = GCNConv(16, num_classes)

    def forward(self, x, edge_index, size):
        # 使用邻居信息进行采样
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv2(x, edge_index, size)
        return F.log_softmax(x, dim=1)


# 加载数据集
dataset = Planetoid(root='/tmp/Cora', name='Cora')
data = dataset[0]

# 创建邻居采样迭代器
train_loader = NeighborSampler(data.edge_index, 
                                node_idx=data.train_mask, 
                                sizes=[10, 10],  # 每层采样的邻居数
                                batch_size=64,   # 每次批次的节点数
                                num_nodes=data.num_nodes)

# 初始化模型和优化器
model = GCN(num_features=dataset.num_node_features, num_classes=dataset.num_classes)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

# 训练模型
def train(loader, model, optimizer):
    model.train()
    total_loss = 0
    for batch_size, n_id, adj in loader:
        optimizer.zero_grad()
        # 通过采样得到节点特征和边列表
        x = data.x[n_id].to(device)
        out = model(x, adj.edge_index, adj.size)
        loss = F.nll_loss(out[adj.node_idx], data.y[adj.node_idx].to(device))
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    return total_loss

# 评估模型
def test(model):
    model.eval()
    with torch.no_grad():
        out = model(data.x.to(device), data.edge_index.to(device), data.num_nodes)
    pred = out.argmax(dim=1)
    correct = pred[data.test_mask.to(device)] == data.y[data.test_mask.to(device)]
    acc = int(correct.sum()) / int(data.test_mask.sum())
    return acc

# 训练和验证
for epoch in range(200):
    loss = train(train_loader, model, optimizer)
    if epoch % 20 == 0:
        train_acc = test(model)
        print(f'Epoch {epoch}, Loss: {loss:.4f}, Train Accuracy: {train_acc:.4f}')

# 测试模型
test_acc = test(model)
print(f'Test Accuracy: {test_acc:.4f}')