可以使用pyg（torch_geometric）搭建GCN网络

PyG（Torch Geometric）是一个用于图神经网络（Graph Neural Networks, GNNs）的Python库，它是基于PyTorch构建的。如果你想使用PyG搭建GCN（Graph Convolutional Network）网络，首先需要安装`torch_geometric`库。以下是一个简单的步骤概述：

1. **安装**:

   pip install torch torchvision torchaudio -f https://download.pytorch.org/whl/torch_stable.html
   pip install torch-scatter -f https://github.com/rusty1s/pytorch_scatter/blobs/main/dist/
   pip install torch-sparse -f https://github.com/rusty1s/pytorch_sparse/blobs/main/dist/
   pip install torch-cluster -f https://github.com/rusty1s/pytorch_cluster/blobs/main/dist/
   pip install torch-geometric

2. **导入所需模块**:

   import torch
   from torch_geometric.nn import GCNConv, global_mean_pool

3. **构建数据结构**:
使用`torch_geometric.data.Data`类来创建图的数据结构，包括节点特征和边的信息。

4. **定义模型**:

   class GCN(torch.nn.Module):
       def __init__(self, in_channels, hidden_channels, out_channels):
           super(GCN, self).__init__()
           self.conv1 = GCNConv(in_channels, hidden_channels)
           self.conv2 = GCNConv(hidden_channels, out_channels)

       def forward(self, data):
           x, edge_index = data.x, data.edge_index
           x = F.relu(self.conv1(x, edge_index))
           x = self.conv2(x, edge_index)
           return global_mean_pool(x, data.batch)

5. **训练和预测**:
定义损失函数、优化器，并通过`DataLoader`加载数据进行训练。

model = GCN(...).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

for epoch in range(100):  # 假设这是训练循环
    optimizer.zero_grad()
    output = model(data)
    loss = F.nll_loss(output[data.train_mask], data.y[data.train_mask])
    loss.backward()
    optimizer.step()

# 预测阶段
with torch.no_grad():
    pred = model(data)