@Tomposon
Given the whole N nodes, you do five-fold cross-validation, i.e., four-fold nodes for training, denoted as the training group T1. We denote the test nodes as T2.
When "Training Percentage # nodes for embedding = 100%", all the four-fold nodes (T1) are used. We build a graph by including nodes in T1 and T2, and the edges between them. We embedding the constructed graph to learn H. And use embedding representations of T1 to train the classifier.
When "Training Percentage # nodes for embedding = 50%", 50% of the nodes in T1 are used to construct the new graph along with T2.
Thanks.
@Tomposon
Given the whole N nodes, you do five-fold cross-validation, i.e., four-fold nodes for training, denoted as the training group T1. We denote the test nodes as T2.
When "Training Percentage # nodes for embedding = 100%", all the four-fold nodes (T1) are used. We build a graph by including nodes in T1 and T2, and the edges between them. We embedding the constructed graph to learn H. And use embedding representations of T1 to train the classifier.
When "Training Percentage # nodes for embedding = 50%", 50% of the nodes in T1 are used to construct the new graph along with T2.
Thanks.