Graph Neural Network Regression
I am trying to implement a regression on a Graph Neural Network. Most of the examples that I see are that of classification in this area, none so far of regression. I saw one for classification as follows:
from torch_geometric.nn import GCNConv
class GCN(torch.nn.Module):
def __init__(self, hidden_channels):
    super(GCN, self).__init__()
    torch.manual_seed(12345)
    self.conv1 = GCNConv(dataset.num_features, hidden_channels)
    self.conv2 = GCNConv(hidden_channels, dataset.num_classes)

def forward(self, x, edge_index):
    x = self.conv1(x, edge_index)
    x = x.relu()
    x = F.dropout(x, p=0.5, training=self.training)
    x = self.conv2(x, edge_index)
    return x

model = GCN(hidden_channels=16)
print(model)

I am trying to modify it for my task, which basically includes performing a regression on a network with 30 nodes, each having 3 features and the edge has one feature.
If anyone could point me to examples to do the same, that would be very helpful.

add a linear layer,and don't forget use a regression loss function
class GCN(torch.nn.Module):
    def __init__(self, hidden_channels):
        super(GCN, self).__init__()
        torch.manual_seed(12345)
        self.conv1 = GCNConv(dataset.num_features, hidden_channels)
        self.conv2 = GCNConv(hidden_channels, dataset.num_classes)
        self.linear1 = torch.nn.Linear(100,1)
    def forward(self, x, edge_index):
        x = self.conv1(x, edge_index)
        x = x.relu()
        x = F.dropout(x, p=0.5, training=self.training)
        x = self.conv2(x, edge_index)
        x = self.linear1(x)
        return x

