Before revision:  Specifically, the first is node features, we design a efficient-spectral-clustering  with anchors (ESC-ANCH) approach to efficiently aggregate feature representationsfrom all similar nodes , no matter how far away they are. The second is edges, our approach generates a re-connected adjacency matrix according to the similarities between nodes and optimized for the downstream prediction task so as to make up for the shortcomings of original adjacency matrix , considering that the original adjacency matrix usually provides misleading information for aggregation step of GCN in the graphs with low level of homophily.
After revision:  Specifically, the first is node features, we design a efficient-spectral-clustering with anchors (ESC-ANCH) approach to efficiently aggregate feature representationsfrom all similar nodes . In the aspect of edges, we build a re-connected adjacency matrix according to the similarities between nodes and optimized for the downstream prediction task so as to make up for the shortcomings of original adjacency matrix , considering that the original adjacency matrix usually provides misleading information for aggregation step of GCN in the graphs with low level of homophily.