pytorch实现SDNE代码

淡淡的烟草味﹌ 2024-03-26 09:14 60阅读 0赞

好的，下面是用 PyTorch 实现 SDNE (Structure Deep Network Embedding) 的代码。

首先，我们需要定义一个 SDNE 模型类。

import torch
    import torch.nn as nn
    
    class SDNE(nn.Module):
        def __init__(self, num_nodes, num_features, hidden_size1, hidden_size2, alpha, beta):
            super(SDNE, self).__init__()
            
            self.encoder = nn.Sequential(
                nn.Linear(num_features, hidden_size1),
                nn.ReLU(),
                nn.Linear(hidden_size1, hidden_size2),
                nn.ReLU()
            )
            self.decoder = nn.Sequential(
                nn.Linear(hidden_size2, hidden_size1),
                nn.ReLU(),
                nn.Linear(hidden_size1, num_features),
                nn.Sigmoid()
            )
            
            self.num_nodes = num_nodes
            self.alpha = alpha
            self.beta = beta
            
        def forward(self, adjacency_matrix):
            embeddings = self.encoder(adjacency_matrix)
            reconstructions = self.decoder(embeddings)
            
            # Calculate reconstruction loss
            reconstruction_loss = self.alpha * self._reconstruction_loss(adjacency_matrix, reconstructions)
            
            # Calculate regularization loss
            regularization_loss = self.beta * self._regularization_loss(embeddings)
            
            return reconstructions, reconstruction_loss, regularization_loss
        
        def _reconstruction_loss(self, input, reconstructions):
            reconstruction_loss = nn.MSELoss()(reconstructions, input)
            return reconstruction_loss
        
        def _regularization_loss(self, embeddings):
            # Calculate the pairwise distance matrix
            pairwise_distance = self._pairwise_distance(embeddings)
            
            # Calculate the adjacency matrix of the k-nearest neighbors
            adjacency_matrix = self._adjacency_matrix(pairwise_distance)
            
            # Calculate the graph laplacian
            laplacian = self._laplacian(adjacency_matrix)
            
            # Calculate the regularization loss
            regularization_loss = torch.trace(torch.mm(embeddings, laplacian) @ embeddings.t())
            
            return regularization_loss
        
        def _pairwise_distance(self, embeddings):
            # Calculate the pairwise distance matrix
            dot_product = torch.mm(embeddings, embeddings.t())
            square_norm = torch.diag(dot_product)
            pairwise_distance = square_norm.unsqueeze(1) - 2 * dot_product + square_norm.unsqueeze(0)