I was recently reading the following paper: "Semi-supervised classification with Graph Convolutional Networks" by Kipf and Welling (here).
Question: When testing on datasets, why are the authors using such a low label rate?
Context: In Table 1 where the authors list the statistics for the datasets they used to benchmark the architecture, the label rates are quite small percentages (5%, 3%, <1%). I don't quite understand why this is the case...
I know this is 'semi-supervised' classification such that we have access to ALL the node features, regardless of whether they are training or testing. However, I don't know why we are using 5% label rate as opposed to, for example, 30% label rate.
Any insight would be appreciated.