e achieve comparable or better performance than recent models on the OGB benchmark datasets, including ogbl-ddi, ogbl-collab, ogbl-ppa, and ogbl-citation2:

Metric	Cora	Citeseer	Pubmed	Collab	PPA	Citation2	DDI
	Hits@100	Hits@100	Hits@100	Hits@50	Hits@100	MRR	Hits@20
CN	$33.92 \pm 0.46$	$29.79 \pm 0.90$	$23.13 \pm 0.15$	$56.44 \pm 0.00$	$27.65 \pm 0.00$	$51.47 \pm 0.00$	$17.73 \pm 0.00$
AA	$39.85 \pm 1.34$	$35.19 \pm 1.33$	$27.38 \pm 0.11$	$64.35 \pm 0.00$	$32.45 \pm 0.00$	$51.89 \pm 0.00$	$18.61 \pm 0.00$
RA	$41.07 \pm 0.48$	$33.56 \pm 0.17$	$27.03 \pm 0.35$	$64.00 \pm 0.00$	$49.33 \pm 0.00$	$51.98 \pm 0.00$	$27.60 \pm 0.00$
SEAL	$81.71 \pm 1.30$	$83.89 \pm 2.15$	$75.54 \pm 1.32$	$64.74 \pm 0.43$	$48.80 \pm 3.16$	$87.67 \pm 0.32$	$30.56 \pm 3.86$
NBFNet	$71.65 \pm 2.27$	$74.07 \pm 1.75$	$58.73 \pm 1.99$	OOM	OOM	OOM	$4.00 \pm 0.58$
Neo-GNN	$80.42 \pm 1.31$	$84.67 \pm 2.16$	$73.93 \pm 1.19$	$57.52 \pm 0.37$	$49.13 \pm 0.60$	$87.26 \pm 0.84$	$63.57 \pm 3.52$
BUDDY	$88.00 \pm 0.44$	$\mathbf{92.93 \pm 0.27}$	$74.10 \pm 0.78$	$65.94 \pm 0.58$	$49.85 \pm 0.20$	$87.56 \pm 0.11$	$78.51 \pm 1.36$
NCN	$\mathbf{89.05 \pm 0.96}$	$91.56 \pm 1.43$	$\underline{79.05 \pm 1.16}$	$64.76 \pm 0.87$	$61.19 \pm 0.85$	$88.09 \pm 0.06$	$\underline{82.32 \pm 6.10}$
MPLP+	-	-	-	$\mathbf{66.99 \pm 0.40}$	$\underline{65.24 \pm 1.50}$	$\mathbf{90.72 \pm 0.12}$	-
GAE(GCN)	$66.79 \pm 1.65$	$67.08 \pm 2.94$	$53.02 \pm 1.39$	$47.14 \pm 1.45$	$18.67 \pm 1.32$	$84.74 \pm 0.21$	$37.07 \pm 5.07$
GAE(SAGE)	$55.02 \pm 4.03$	$57.01 \pm 3.74$	$39.66 \pm 0.72$	$54.63 \pm 1.12$	$16.55 \pm 2.40$	$82.60 \pm 0.36$	$53.90 \pm 4.74$
Optimized-GAE	$\underline{88.17 \pm 0.93}$	$\underline{92.40 \pm 1.23}$	$\mathbf{80.09 \pm 1.72}$	$\underline{66.11 \pm 0.35}$	$\mathbf{78.41 \pm 0.83}$	$\underline{88.74 \pm 0.06}$	$\mathbf{94.43 \pm 0.57}$

The code is based on the DGL library and the OGB library. To run the code, you need to set up the environment specified in the env.yaml file:

conda env create -f env.yaml

python train_w_feat_small.py --dataset Cora --lr 0.01 --prop_step 2 --hidden 512 --batch_size 4096 --dropout 0.2 --num_neg 3 --epoch 100 --metric hits@100 --model LightGCN --norm

python train_w_feat_small.py --dataset CiteSeer --lr 0.01 --prop_step 2 --hidden 512 --batch_size 4096 --dropout 0.2 --num_neg 3 --epoch 200 --metric hits@100 --model LightGCN --norm

python train_w_feat_small.py --dataset PubMed --lr 0.005 --prop_step 2 --hidden 512 --batch_size 4096 --dropout 0.2 --num_neg 3 --epoch 100 --metric hits@100 --model LightGCN --mlp_layers 5 --res --norm --exp --alpha 0.5

Below we give the commands to run the code on the four datasets in the OGB benchmark.

python train_wo_feat.py --dataset ogbl-ddi --lr 0.001 --hidden 1024 --batch_size 8192 --dropout 0.6 --num_neg 1 --epochs 500 --prop_step 2 --metric hits@20 --residual 0.1 --maskinput --mlp_layers 8 --mlp_res --emb_dim 1024

python collab.py --dataset ogbl-collab --lr 0.0004 --emb_hidden 0 --hidden 1024 --batch_size 16384 --dropout 0.2 --num_neg 3 --epoch 500 --prop_step 4 --metric hits@50 --mlp_layers 5 --res --gpu 1 --norm --dp4norm 0.2 --scale

python train_wo_feat.py --dataset ogbl-ppa --lr 0.001 --hidden 512 --batch_size 65536 --dropout 0.2 --num_neg 3 --epoch 800 --prop_step 2 --metric hits@100 --residual 0.1 --mlp_layers 5 --mlp_res --emb_dim 512

python citation.py --dataset ogbl-citation2 --lr 0.0003 --clip_norm 1 --emb_hidden 256 --hidden 256 --batch_size 65536 --dropout 0.2 --num_neg 6 --epochs 200 --prop_step 3 --metric MRR --norm --dp4norm 0.2 --mlp_layers 5

For ogbl-citation2 dataset, you need a GPU with at least 40GB memory.