• Compatible with PyTorch 1.0 and Python 3.x.
• Dependencies can be installed using requirements.txt.

• We use FB15k-237,FB15k,DB100K,Kinship,WN18RR,WN18 datasets for evaluation, and they are included in the repo.

• Install all the requirements from requirements.txt.
• Execute sh preprocess.sh for extracting the datasets and setting up the environment.
• The command for training arce and its prompt are below:
  • data indicates the dataset used for training the model.
  • gpu is the GPU used for training the model.
  • name is the provided name of the run which can be later used for restoring the model.
  • Execute python acre.py --help for listing all the available options.

    #Serial fb15k237
    python -u acre.py --data FB15k-237 --batch 128 \
    --hid_drop 0.5 --feat_drop 0.2 --lr 0.001 --inp_drop 0.3  --gpu 0 --name fb15k_237_s --way s --train_strategy one_to_x
    
    #Serial fb15k
    python -u acre.py --data FB15k --batch 256 \
    --hid_drop 0.2 --feat_drop 0.2 --lr 0.001 --inp_drop 0.2  --gpu 0 --name fb15k_s --way s --train_strategy one_to_n
    
    #Serial kinship
    python -u acre.py --data kinship --batch 128 \
    --hid_drop 0.5 --feat_drop 0.5 --lr 0.001 --inp_drop 0.2  --gpu 0 --name kinship_s --way s --train_strategy one_to_n
    
    #Serial WN18RR
    python -u acre.py --data WN18RR --batch 256 \
    --hid_drop 0.5 --feat_drop 0.1 --lr 0.00125 --inp_drop 0.2  --gpu 0 --name wn18rr_s --way s --train_strategy one_to_n
    
    #Serial WN18
    python -u acre.py --data WN18 --batch 256 \
    --hid_drop 0.3 --feat_drop 0.3 --lr 0.0012 --inp_drop 0.2  --gpu 0 --name wn18_s --way s  --train_strategy one_to_n
    
    #Serial DB100K
    python -u acre.py --data DB100K --batch 256 \
    --hid_drop 0.3 --feat_drop 0.2 --lr 0.0012 --inp_drop 0.2  --gpu 0 --name db100k_s --way s --train_strategy one_to_x
    
    #Parallel fb15k237
    python -u acre.py --data FB15k-237 --batch 128 \
    --hid_drop 0.5 --feat_drop 0.2 --lr 0.001 --inp_drop 0.3  --gpu 0 --name --fb15k_237_p --way p --train_strategy one_to_x
    
    #Parallel fb15k
    python -u acre.py --data FB15k --batch 256 \
    --hid_drop 0.2 --feat_drop 0.2 --lr 0.001 --inp_drop 0.2  --gpu 0 --name --fb15k_p --way p  --train_strategy one_to_n
    
    #Parallel kinship
    python -u acre.py --data kinship --batch 128 \
    --hid_drop 0.5 --feat_drop 0.2 --lr 0.0001 --inp_drop 0.3  --gpu 0 --name --kinship_p --way p  --train_strategy one_to_n
    
    #Parallel WN18RR
    python -u acre.py --data WN18RR --batch 256 \
    --hid_drop 0.5 --feat_drop 0.1 --lr 0.00125 --inp_drop 0.3  --gpu 0 --name --wn18rr_p --way p --train_strategy one_to_x
    
    #Parallel WN18
    python -u acre.py --data WN18 --batch 256 \
    --hid_drop 0.3 --feat_drop 0.3 --lr 0.0012 --inp_drop 0.2 --gpu 0 --name --wn18_p --way p --train_strategy one_to_x
    
    #Parallel DB100K
    python -u acre.py --data DB100K --batch 256 \
    --hid_drop 0.3 --feat_drop 0.2 --lr 0.0012 --inp_drop 0.2  --gpu 0 --name --db100k_p --way p --train_strategy one_to_x
    

Parts of our codes come from InteractE. Thanks for their contributions.