Before revision:  2)  the smaller scale of training data is beneficial to reduce the human resource cost to label them ; The extensive experiments show that the sampling-based methodcan achieve the same performance only with 10 \\%DIF < -50\\% of the scale of training data.
After revision:  2) the smaller scale of training data is beneficial to reduce the human resource cost to label them . We choose six popular GCN baselines and conduct extensive experiments on three real-world datasets. The results show that by applying our method, all GCN baselines cut down the annotation requirement by as much as 90 \\%DIF < -50\\% of the scale of training data.