写图卷积神经网络的代码

对于图卷积神经网络的代码实现，可以使用PyTorch进行编写，代码如下：

import torch
import torch.nn as nn

class GraphConvolution(nn.Module):
    def __init__(self, in_features, out_features):
        super(GraphConvolution, self).__init__()
        self.weight = nn.Parameter(torch.FloatTensor(in_features, out_features))
        self.bias = nn.Parameter(torch.FloatTensor(out_features))
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.xavier_uniform_(self.weight)
        nn.init.zeros_(self.bias)

    def forward(self, input, adj):
        support = torch.mm(input, self.weight)
        output = torch.spmm(adj, support)
        return output + self.bias

class GCN(nn.Module):
    def __init__(self, nfeat, nhid, nclass, dropout):
        super(GCN, self).__init__()

        self.gc1 = GraphConvolution(nfeat, nhid)
        self.gc2 = GraphConvolution(nhid, nclass)
        self.dropout = dropout

    def forward(self, x, adj):
        x = F.relu(self.gc1(x, adj))
        x = F.dropout(x, self.dropout, training=self.training)
        x = self.gc2(x, adj)
        return F.log_softmax(x, dim=1)

其中`GraphConvolution`是图卷积层的实现，`GCN`是整个图卷积神经网络的实现。在`GraphConvolution`中，使用了稀疏矩阵乘法，使用了`torch.spmm`进行实现。在`GCN`中，使用了两层`GraphConvolution`和`dropout`操作。最后输出的是`log_softmax`。