AM-GCN: Adaptive Multi-channel Graph Convolutional Networks (AM-GCN) Paper link: https://dl.acm.org/doi/10.1145/3394486.3403177 Author's code repo: https://github.com/BUPT-GAMMA/AM-GCN. Note that the original code is implemented with PyTorch for the paper. Dataset Statics Dataset # Nodes # Edges # Classes Cora 2,708 10,556 7 Citeseer 3,327 9,228 6 Pubmed 19,717 88,651 3 Refer to Planetoid. Results # available dataset: "cora", "citeseer", "pubmed" TL_BACKEND=torch python amgcn_trainer.py --dataset cora --lr 0.0005 --k 6 --hidden1 512 --hidden2 32 --drop_rate 0.5 --beta 1e-10 --theta 0.0001 TL_BACKEND=torch python amgcn_trainer.py --dataset citeseer --lr 0.0005 --k 7 --hidden1 768 --hidden2 256 --drop_rate 0.5 --beta 5e-10 --theta 0.001 TL_BACKEND=torch python amgcn_trainer.py --dataset pubmed --lr 0.0005 --k 6 --hidden1 512 --hidden2 128 --drop_rate 0.5 --beta 5e-10 --theta 0.001 Dataset Paper Our(th) cora 79.5(±0.3) citeseer 73.1 71.7(±1.2) pubmed 64.4(±0.8)