Using Customized GNN
GNN layers in CogDL to Define model
CogDL has implemented popular GNN layers in cogdl.layers, and they can serve as modules to help design new GNNs. Here is how we implement Jumping Knowledge Network (JKNet) with GCNLayer in CogDL. JKNet collects the output of all layers and concatenate them together to get the result:
import torch from cogdl.layers import GCNLayer from cogdl.models import BaseModel class JKNet(BaseModel): def __init__(self, in_feats, out_feats, hidden_size, num_layers): super(JKNet, self).__init__() shapes = [in_feats] + [hidden_size] * num_layers self.layers = nn.ModuleList([ GCNLayer(shapes[i], shapes[i+1]) for i in range(num_layers) ]) self.fc = nn.Linear(hidden_size * num_layers, out_feats) def forward(self, graph): # symmetric normalization of adjacency matrix graph.sym_norm() h = graph.x out =  for layer in self.layers: h = layer(graph,h) out.append(h) out = torch.cat(out, dim=1) return self.fc(out)
Define your GNN Module
In most cases, you may build a layer module with new message propagation and aggragation scheme. Here the code snippet shows how to implement a GCNLayer using Graph and efficient sparse matrix operators in CogDL.
import torch from cogdl.utils import spmm class GCNLayer(torch.nn.Module): """ Args: in_feats: int Input feature size out_feats: int Output feature size """ def __init__(self, in_feats, out_feats): super(GCNLayer, self).__init__() self.fc = torch.nn.Linear(in_feats, out_feats) def forward(self, graph, x): h = self.fc(x) h = spmm(graph, h) return h
Use Custom models with CogDL
Now that you have defined your own GNN, you can use dataset/task in CogDL to immediately train and evaluate the performance of your model.
data = build_dataset_from_name("cora") # Use the JKNet model as defined above model = JKNet(data.num_features, data.num_classes, 32, 4) experiment(model=model, dataset="cora", mw="node_classification_mw", dw="node_classification_dw")
Total running time of the script: ( 0 minutes 0.000 seconds)