heco

Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning

• Paper link: https://arxiv.org/abs/2105.09111
• Author's code repo: https://github.com/liun-online/HeCo

Dataset Statics

Dataset	# Nodes_paper	# Nodes_author	# Nodes_subject
ACM	4019	7167	60

Refer to ACM.

Results For ACM

TL_BACKEND="torch" python HeCo_trainer.py --dataset acm --hidden_dim 64  --nb_epochs 10000 --eva_lr 0.05 --lr 0.0075 --l2_coef 0 --tau 0.8 --lam 0.5 --feat_drop 0.3 --attn_drop 0.3
TL_BACKEND="paddle" python HeCo_trainer.py --dataset acm --hidden_dim 64  --nb_epochs 10000 --eva_lr 0.05 --lr 0.0075 --l2_coef 0 --tau 0.8 --lam 0.5 --feat_drop 0.3 --attn_drop 0.3
TL_BACKEND="tensorflow" python HeCo_trainer.py --dataset acm --hidden_dim 64  --nb_epochs 10000 --eva_lr 0.05 --lr 0.0075 --l2_coef 0 --tau 0.8 --lam 0.5 --feat_drop 0.3 --attn_drop 0.3

• Ma-F1

number of train_labels	Paper	Our(tf)	Our(pd)	Our(torch)
20	88.56±0.8	84.7±0.4	85.0±0.4	85.0±0.3
40	87.61±0.5	88.1±0.3	88.63±0.1	88.64±0.2
60	89.04±0.5	87.4±0.4	88.3±0.4	88.4±0.6

• Mi-F1

number of train_labels	Paper	Our(tf)	Our(pd)	Our(torch)
20	88.13±0.8	84.1±0.4	84.8±0.8	85.0±0.4
40	87.45±0.5	87.9±0.3	88.43±0.1	88.53±0.6
60	88.71±0.5	87.4±0.4	88.2±0.5	88.45±0.6

• AUC

number of train_labels	Paper	Our(tf)	Our(pd)	Our(torch)
20	96.49±0.3	93.8±0.4	95.1±0.4	95.3±0.3
40	96.4±0.4	96.4±0.3	97.1±0.2	97.4±0.3
60	96.55±0.3	95.8±0.4	96.4±0.4	96.7±0.4

For TensorFlow runs more slowly than paddlepaddle and pytorch, thus pd and torch are more recommended.