  We propose training-free graph neural networks (TFGNNs), which can be used
without training and can also be improved with optional training, for
transductive node classification. We first advocate labels as features (LaF),
which is an admissible but not explored technique. We show that LaF provably
enhances the expressive power of graph neural networks. We design TFGNNs based
on this analysis. In the experiments, we confirm that TFGNNs outperform
existing GNNs in the training-free setting and converge with much fewer
training iterations than traditional GNNs.
