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Adversarial Contrastive Graph Augmentation with Counterfactual Regularization

Implementation for AAAI'25 paper: Adversarial Contrastive Graph Augmentation with Counterfactual Regularization

environment install:

pip install -r requirements.txt

Parameter introduction:

--dataset  cora or citeseer or airpotr ...
--hidden_size encoder hidden dimension, node clsssification defalut 128,link prediction defalut 32
--emb_size VGAE/GAE embedding dimension,node clsssification defalut 32,link prediction defalut 16
--gae selct VGAE or GAE
--use_bns use bn
--task 0 is node clsssification, and 1 is link prediction
--alpha
--beta
--gamma optuna get best parameter

Run parameter search node classification:

python optuna_ACGA.py --dataset cora --hidden_size 128 --emb_size 32 --gae 0 --use_bns True --task 0

Run parameter search link prediction:

python optuna_ACGA.py --dataset cora --hidden_size 32 --emb_size 16 --gae 0 --use_bns True --task 1

Run the node classification task:

python ACGA.py --dataset cora --hidden_size 128 --emb_size 32 --gae 0 --use_bns True --task 0

Run the link prediction task:

python ACGA.py --dataset cora --hidden_size 32 --emb_size 16 --gae 0 --use_bns True --task 1

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