  The original design of Graph Convolution Network (GCN) couples feature
transformation and neighborhood aggregation for node representation learning.
Recently, some work shows that coupling is inferior to decoupling, which
supports deep graph propagation better and has become the latest paradigm of
GCN (e.g., APPNP and SGCN). Despite effectiveness, the working mechanisms of
the decoupled GCN are not well understood. In this paper, we explore the
decoupled GCN for semi-supervised node classification from a novel and
fundamental perspective -- label propagation. We conduct thorough theoretical
analyses, proving that the decoupled GCN is essentially the same as the
two-step label propagation: first, propagating the known labels along the graph
to generate pseudo-labels for the unlabeled nodes, and second, training normal
neural network classifiers on the augmented pseudo-labeled data. More
interestingly, we reveal the effectiveness of decoupled GCN: going beyond the
conventional label propagation, it could automatically assign structure- and
model- aware weights to the pseudo-label data. This explains why the decoupled
GCN is relatively robust to the structure noise and over-smoothing, but
sensitive to the label noise and model initialization. Based on this insight,
we propose a new label propagation method named Propagation then Training
Adaptively (PTA), which overcomes the flaws of the decoupled GCN with a dynamic
and adaptive weighting strategy. Our PTA is simple yet more effective and
robust than decoupled GCN. We empirically validate our findings on four
benchmark datasets, demonstrating the advantages of our method. The code is
available at https://github.com/DongHande/PT_propagation_then_training.
