# This is for reproduce the results on the paper
# 

# On three citation networks
# Run SR-GNN on biased training data, snowball-sample=True using the biased training data
python main_gnn.py --dataset=cora --SR=True --gnn-arch=ppnp --n-repeats=20 --n-epochs=200 --n-hidden=32 --snowball-sample=True --dropout=0.5 --weight-decay=0.0005
python main_gnn.py --dataset=citeseer --SR=True --gnn-arch=ppnp --n-repeats=20 --n-epochs=200 --n-hidden=32 --snowball-sample=True --dropout=0.5 --weight-decay=0.0005
python main_gnn.py --dataset=pubmed --SR=True --gnn-arch=ppnp --n-repeats=20 --n-epochs=200 --n-hidden=32 --snowball-sample=True --dropout=0.5 --weight-decay=0.0005

# Run SR-GNN with biased training data on large dataset
python main_gnn_large.py --dataset=ogbn-arxiv --gnn-arch=ppnp --n-repeats=20 --bn=False --weight-decay=0.0005 --n-epochs=50 --n-hidden=256 --dropout=0.5


# Examples of running baselines
#DGI
python main_gnn_baseline.py --dataset=cora --gnn-arch=dgi --n-repeats=20 --n-epochs=200 --n-hidden=512 --snowball-sample=True
#Features
python main_gnn_baseline.py --dataset=cora --gnn-arch=features --n-repeats=20 --n-epochs=200 --n-hidden=32 --snowball-sample=True
#Deepwalk embeddings
python main_gnn_baseline.py --dataset=cora --gnn-arch=emb --n-repeats=20 --n-epochs=200 --n-hidden=32 --snowball-sample=True

