On the Equivalence of Decoupled Graph Convolution Network and Label Propagation

Understand

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.