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It seems in GNN(graph neural network), in transductive situation, we input the whole graph and we mask the label of valid data and predict the label for the valid data.

But is seems in inductive situation, we also input the whole graph(but sample to batch) and mask the label of the valid data and predict the label for the valid data.

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In inductive learning, during training you are unaware of the nodes used for testing. For the specific inductive dataset here (PPI), the test graphs are disjoint and entirely unseen by the GNN during training.

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    $\begingroup$ It would be great to add some examples of inductive, transductive and both inductive and transductive algorithms $\endgroup$ Commented Mar 22, 2022 at 9:29

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