Method forward

gcn/main.py:89–112

Performs forward pass of the Graph Convolutional Network (GCN). Args: input_tensor (torch.Tensor): Input node feature matrix with shape (N, input_dim), where N is the number of nodes and input_dim is the number of input features per node. adj

(self, input_tensor, adj_mat)

        self.dropout = nn.Dropout(dropout_p)

    def forward(self, input_tensor, adj_mat):
        """
        Performs forward pass of the Graph Convolutional Network (GCN).

        Args:
            input_tensor (torch.Tensor): Input node feature matrix with shape (N, input_dim), where N is the number of nodes
                and input_dim is the number of input features per node.
            adj_mat (torch.Tensor): Normalized adjacency matrix of the graph with shape (N, N), representing the relationships between
                nodes.

        Returns:
            torch.Tensor: Output tensor with shape (N, output_dim), representing the predicted class probabilities for each node.
        """

        # Perform the first graph convolutional layer
        x = self.gc1(input_tensor, adj_mat)
        x = F.relu(x) # Apply ReLU activation function
        x = self.dropout(x) # Apply dropout regularization

        # Perform the second graph convolutional layer
        x = self.gc2(x, adj_mat)

        # Apply log-softmax activation function for classification
        return F.log_softmax(x, dim=1)


def load_cora(path='./cora', device='cpu'):