You can use Pytorch_Geometric library for your projects. Its supports weighted GCNs. It is a rapidly evolving open-source library with easy to use syntax. It is mentioned in the landing page of Pytorch. It is the most starred Pytorch github repo for geometric deep learning. Creating a GCN model which can process graphs with weights is as simple as:
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
self.conv1 = GCNConv(dataset.num_node_features, 16)
self.conv2 = GCNConv(16, dataset.num_classes)
def forward(self, data):
# data has the following 3 attributes
x, edge_index, edge_weight = data.x, data.edge_index, data.edge_weight
x = self.conv1(x, edge_index, edge_weight)
x = F.relu(x)
x = F.dropout(x, training=self.training)
x = self.conv2(x, edge_index, edge_weight)
return F.log_softmax(x, dim=1)
See this for getting started.
Check out its documentation on different variants of GCNs for further details. One of the best thing is that like Pytorch, its documentation are self-sufficient.