Hi @czkkkkkk and thanks for your fast reply !
Okay I’m confused… Why is there many articles stating that GCN is an inductive learning framework ?
And, even two of you collaborators think it is inductive, check this post How to carry out the batch-wise training while using layer defined in DGL? - Questions - Deep Graph Library (neo and mufeili).
You can also see on this website : Graph Neural Networks (snap-stanford.github.io) which comes from Stanford university and where Jure Leskovec teaches this course, that GCN has an inductive capacity.
From my point of view, I think we can use GCN as an inductive method because it does not change the shape of weight matrixes when we add a new node to the graph. We just need to do get the node’ neighbors times their features times the learned weight matrixes. In the wend we just have to pass GCN(A[new_nodes], X, W_learned), with A[new_nodes] the line allowing to get the node’ neighbors, X the feature matrix and W_learned all the matrixes learned during the training of the model.
I am really confused as you can see, beacause some say it is transductive, others say it is inductive. I know that originally it was tested in an transductive setting, but many sources accord to say it can be used inductively. Maybe it is because everyone does not have the same definition of transductive and inductive, yet they are straightforward definitions…
In the end, I just want to know if a GCN model can, from a node totally unseen (not masked, really unseen) during the training, generate an embedding for this unseen node ?
At the end of my GCN, I want to classify my node as 1 or 0, so I just put a linear layer with a sigmoid activation function.
Thank you for your clarification.