超图神经网络python代码

以下是一个简单的超图神经网络的Python代码示例：

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

class HyperGraphConv(nn.Module):
    def __init__(self, in_features, out_features):
        super(HyperGraphConv, self).__init__()
        self.linear = nn.Linear(in_features, out_features)

    def forward(self, x, edges):
        edge_weights = torch.sum(edges, dim=1) # 汇总每个超边的权重
        x = torch.matmul(edges.transpose(1, 2), x) # 超图传播
        x = self.linear(x) # 线性变换
        x = F.relu(x) # 激活函数
        x = torch.matmul(edges, x) / edge_weights.unsqueeze(2) # 超图聚合
        return x

class HyperGraphNet(nn.Module):
    def __init__(self, in_features, hidden_features, out_features):
        super(HyperGraphNet, self).__init__()
        self.conv1 = HyperGraphConv(in_features, hidden_features)
        self.conv2 = HyperGraphConv(hidden_features, out_features)

    def forward(self, x, edges):
        x = self.conv1(x, edges)
        x = self.conv2(x, edges)
        return x

# 示例使用
input_size = 32
hidden_size = 64
output_size = 10

x = torch.randn(16, input_size) # 输入特征向量
edges = torch.randn(16, 8, input_size) # 超边连接矩阵

model = HyperGraphNet(input_size, hidden_size, output_size)
output = model(x, edges)

print(output.size()) # 输出: torch.Size([16, 10])

以上示例代码实现了一个简单的超图神经网络，包含了超图卷积层（`HyperGraphConv`）和超图网络模型（`HyperGraphNet`）。在示例中，使用了输入特征向量 `x` 和超边连接矩阵 `edges` 进行前向传播，并输出最终的预测结果 `output`。注意，这只是一个简单的示例，实际应用中可能需要根据具体问题进行适当的修改和扩展。