Thanks @mufeili,

You have been so much helpful for me, and do apologize if I have shot you with plenty of questions. To confirm my understanding about this, I proceed with this implementation

trained_model = torch.load("./mod_gcn.pt")

inference = trained_model(g_infer, g_infer.ndata[“nodeFeat”], g_infer.edata[“edgeFeat”])

interference_prob= torch.sigmoid(inference)

*—>tensor([[0.5556],[0.5381],[0.9999],[0.9905]], grad_fn=<SigmoidBackward>)*

- The result of the sigmoid is the list of probabilities for all 4 nodes in the graph that its node I predict (correct me if I’m wrong). But how do I know which class (binary 0 or 1) that those probabilities are representing?
- And can you point to the example of early stopping for training the binary classification?

Thanks a lot