#Regular-size datasets GSL-LH

In datafold Regular-size. example: 'python train.py --gcn --seed 42 --dataset cora --attack meta --ptb_rate 0.25
--weight_sparsity {weight_sparsity} --adj_sparsity {adj_sparsity}
--lr {lr} --lr_adj {lr_adj} --lr_mask {lr_mask} --seed {seed}' other baseline model in test_{baselinemodel}.py

##Large-scale datasets GSL-LH #'python FatherGraph.py' to make large graph sampling. In datafold Large-arxiv. example: 'python gnn.py --attack --select_attack --save_lottery_graph
--ptb_rate {ptb_rate} --device {device} --gcn_masked
--lr {lr} --lr_adj {lr_adj} --lr_weight {lr_weight} --seed {seed}
--weight_sparsity {weight_sparsity} --adj_sparsity {adj_sparsity} --runs 5'

#Arxiv baselines

In datafold arxiv-baselines. From OGB. example: 'python gnn.py'

#PPRGO-based methods

In datafold LTH_for_arxiv. example: set SparseTensor adjacent matrix saving path=XXX.pt to use learned graph in PPRGO, use 'python run.py --attacked_graph_path=XXX.pt'