异构图卷积神经网络代码实现

异构图卷积神经网络（Heterogeneous Graph Convolutional Network，HGCN）是一种用于处理异构图数据的深度学习模型。下面是异构图卷积神经网络的代码实现示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class HGCNLayer(nn.Module):
    def __init__(self, in_dim, out_dim):
        super(HGCNLayer, self).__init__()
        self.linear = nn.Linear(in_dim, out_dim)

    def forward(self, x):
        return self.linear(x)

class HGCN(nn.Module):
    def __init__(self, num_node_types, num_edge_types, hidden_dim, output_dim):
        super(HGCN, self).__init__()
        self.node_embeddings = nn.ModuleList([nn.Embedding(num_node_types[i], hidden_dim) for i in range(num_node_types)])
        self.edge_embeddings = nn.ModuleList([nn.Embedding(num_edge_types[i], hidden_dim) for i in range(num_edge_types)])
        self.layers = nn.ModuleList([HGCNLayer(hidden_dim, hidden_dim) for _ in range(2)])
        self.output_layer = HGCNLayer(hidden_dim, output_dim)

    def forward(self, node_inputs, edge_inputs, adjacency_matrix):
        x = torch.cat([self.node_embeddings[i](node_inputs[:, i]) for i in range(len(self.node_embeddings))], dim=1)
        edge_embeddings = [self.edge_embeddings[i](edge_inputs[:, i]) for i in range(len(self.edge_embeddings))]
        for layer in self.layers:
            x = F.relu(layer(x))
            x = torch.matmul(adjacency_matrix, x)
            x = torch.cat([x] + edge_embeddings, dim=1)
        x = self.output_layer(x)
        return x

# 示例用法
num_node_types = [10, 5]  # 每种节点类型的数量
num_edge_types = [3, 2]  # 每种边类型的数量
hidden_dim = 64  # 隐藏层维度
output_dim = 10  # 输出维度

node_inputs = torch.tensor([[1, 2], [3, 4]])  # 节点输入特征
edge_inputs = torch.tensor([[0, 1], [1, 0]])  # 边输入特征
adjacency_matrix = torch.tensor([[0, 1], [1, 0]])  # 邻接矩阵

model = HGCN(num_node_types, num_edge_types, hidden_dim, output_dim)
output = model(node_inputs, edge_inputs, adjacency_matrix)
print(output)

这是一个简单的异构图卷积神经网络的代码实现示例，其中包括了节点嵌入层、边嵌入层、HGCN层和输出层。你可以根据自己的需求进行修改和扩展。