Before revision:  However, GCN faces challenges in flexibility due to the fact of requiring the full graph Laplacian available in the training phase. Moreover, with the depth of layers increases, the computational and memory cost of GCN grows explosively on account of the recursive neighborhood expansion , which leads to a limitation in processing large graphs .
After revision:  However, GCN-based models (GCNs) is an inherently coupled training framework repetitively conducting the complex neighboring aggregation, which leads to the limitation of flexibility in processing large-scale graph. With the depth of layers increases, the computational and memory cost of GCN grows explosively on account of the recursive neighborhood expansion , which leads to a limitation in processing large graphs .