Before revision:  In the proposed framework, the traditional graph convolution operation is generalized into two parts: feature transformation and feature aggregation, which gives  CTGCN more flexibility and enables  CTGCN to learn connective and structural information under the same framework.
After revision:  In the proposed framework, the traditional graph convolution is generalized into two parts: feature transformation and feature aggregation, which gives CTGCN more flexibility and enables CTGCN to learn connective and structural information under the same framework.