Before revision:  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:  The second is edges, our approach generates a re-connected adjacency matrix by using a special data preprocessing technique and similarity learning, and the re-connected adjacency matrix can be optimized directly along with GCN-SL parameters. Considering that the original adjacency matrix usually provides misleading information for aggregation step of GCN in the graphs with low level of homophily.