On the Equivalence of Decoupled Graph Convolution Network and Label Propagation

  title={On the Equivalence of Decoupled Graph Convolution Network and Label Propagation},
  author={Hande Dong and Jiawei Chen and Fuli Feng and Xiangnan He and Shuxian Bi and Zhaolin Ding and Peng Cui},
  journal={Proceedings of the Web Conference 2021},
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 [16] and SGCN [32]). 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… 

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